Abstract:Objective We aim to determine the efficiency of CT in identification of cystic echinococcosis in sheep. Methods Fifty-three sheep with liver cysts confirmed by ultrasonography were subject to CT scan to evaluate the number, size, and type of the cysts in liver and lung, confirmed using necropsy. The correlation of numbers between liver cysts and lung cysts was calculated using Pearson analysis. Results Necropsy indicated a 98% consensus on size, location, number, and activity compared with CT scan. The viable … Show more
“…Besides the sensitivity, specificity, and accuracy of PCA‐SVM methods showed that these indexes were better than some commonly used methods for diagnosing CE in sheep, for instance, B‐ultrasound (sensitivity 75%–88%, specificity 70%–76%) [53], and ELISA detection (sensitivity 55%–60%, specificity 67%–70%) [54]. There were some drawbacks in the B‐ultrasound detection technology, such as the need to trim the sheep wool, the difficulty in operation, the need for expensive imaging equipment, and professional imaging physicians [55]. Serological testing was difficult to standardize and has low sensitivity and specificity [54].…”
Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data.Among all the bands, 1500-1700 cm À1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.
“…Besides the sensitivity, specificity, and accuracy of PCA‐SVM methods showed that these indexes were better than some commonly used methods for diagnosing CE in sheep, for instance, B‐ultrasound (sensitivity 75%–88%, specificity 70%–76%) [53], and ELISA detection (sensitivity 55%–60%, specificity 67%–70%) [54]. There were some drawbacks in the B‐ultrasound detection technology, such as the need to trim the sheep wool, the difficulty in operation, the need for expensive imaging equipment, and professional imaging physicians [55]. Serological testing was difficult to standardize and has low sensitivity and specificity [54].…”
Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data.Among all the bands, 1500-1700 cm À1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.
“…Mao et al in 2017 determined the efficiency of CT scan modality in the identification of CE in sheep. The results indicated that CT is a suitable tool for determining the size and type of CE cysts in the liver and lungs of sheep [ 66 ]. The utilization of CT scans for detecting and characterizing CE cysts is very limited to humans and sheep.…”
Background
This study aimed to determine the therapeutic efficacy of curcumin nanoemulsion (CUR-NE) in mice infected with Echinococcus granulosus sensu stricto protoscoleces.
Methods
Forty-two inbred BALB/c mice were divided into seven groups of six animals each. Six groups were inoculated intra-peritoneally with 1500 viable E. granulosus protoscoleces, followed for six months and used as infected groups. The infected groups were named as: CEI1 to CEI6 accordingly. The 7th group was not inoculated and was named cystic echinococcosis noninfected group (CENI7). CEI1 and CEI2 groups received 40 mg/kg/day and 20 mg/kg/day curcumin nanoemulsion (CUR-NE), respectively. CEI3 received nanoemulsion without curcumin (NE-no CUR), CEI4 received curcumin suspension (CUR-S) 40 mg/kg/day, CEI5 received albendazole 150 mg/kg/day and CEI6 received sterile phosphate-buffered saline (PBS). CENI7 group received CUR-NE 40 mg/kg/day. Drugs administration was started after six months post-inoculations of protoscoleces and continued for 60 days in all groups. The secondary CE cyst area was evaluated by computed tomography (CT) scan for each mouse before treatment and on the days 30 and 60 post-treatment. The CT scan measurement results were compared before and after treatment. After the euthanasia of the mice on the 60th day, the cyst area was also measured after autopsy and, the histopathological changes of the secondary cysts for each group were observed. The therapeutic efficacy of CUR-NE in infected groups was evaluated by two methods: CT scan and autopsied cyst measurements.
Results
Septal calcification in three groups of infected mice (CEI1, CEI2, and CEI4) was revealed by CT scan. The therapeutic efficacy of CUR-NE 40 mg/kg/day (CEI1 group) was 24.6 ± 26.89% by CT scan measurement and 55.16 ± 32.37% by autopsied cysts measurements. The extensive destructive effects of CUR-NE 40 mg/kg/day (CEI1 group) on the wall layers of secondary CE cysts were confirmed by histopathology.
Conclusion
The current study demonstrated a significant therapeutic effect of CUR-NE (40 mg/kg/day) on secondary CE cysts in BALB/c mice. An apparent septal calcification of several cysts revealed by CT scan and the destructive effect on CE cysts observed in histopathology are two critical key factors that suggest curcumin nanoemulsion could be a potential treatment for cystic echinococcosis.
“…In small animals, except for the CNS, CT usually represents a second level modality for studying the skeleton, particularly the axial one (skull and spine), the thorax and the abdomen [3] . The use of CT is also described for equine [19] , cattle [20] , goat [21] , sheep [22] , swine [23] , avian and chelonian [11] , reptiles [24] , rabbit and rodents [25] . As in human medicine, CT can be used as bone densitometer [26] .…”
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