CASE OF FASCIOLIASIS: EXTRACTION WITH ENDOSCOPIC RETROGRADE CHOLANGIOGRAPHY

Fascioliasis is a worldwide zoonotic infection with fasciola hepatica and fasciola gigantica. The zoonoses are particularly endemic in sheep‐raising countries and are also endemic in Turkey. Clinical features of fascioliasis relate to the stage and intensity of infection. Fasciola hepatica infection comprises two stages: hepatic and biliary, with different signs and symptoms. Cholestatic symptoms may be sudden, but, in some cases, they may be preceded by a long period of fever, eosinophilia and vague gastrointestinal symptoms. We reported a case with fever and upper‐quadrant abdominal pain since 3 months that comes from an area endemic for fasciola hepatica, with suspected imaging about fasciola hepatica in common bile duct on ultrasonography. After that, fasciola hepatica was extracted with endoscopic retrograde cholangiography.     

Age-dependent changes in CXC chemokine ligand 10 serum levels in euthyroid subjects.

Circulating levels of cytokines are deeply influenced by aging, and few data about serum chemokines are available. The aim of this study was to evaluate the influence of aging on circulating CXCL10. One hundred forty healthy subjects (70 males and 70 females), 10-79 years of age, underwent fasting plasma glucose, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride, and CXCL8 serum assay. Thyroid hormone testing for thyroid-stimulating hormone (TSH), antithyroglobulin (AbTg), and antithyroperoxidase (AbTPO) autoantibodies and thyroid ultrasonography were performed in all subjects to exclude the presence of clinical or subclinical thyroid disease. Serum CXCL10 levels were assayed in all subjects and found to be increased in 14 of 70 females (20%) and in 4 of 70 males (5.7%) (p = 0.01). In a multiple linear regression model including age, body mass index (BMI), systolic and diastolic blood pressure, glycemia, total cholesterol, HDL, LDL, triglycerides, TSH, AbTPO, AbTg, and CXCL8, only age was significantly related to CXCL10 [C.R. 1.30 (0.28-2.33), p = 0.001]. No relationship was present between CXCL8 serum levels and age, suggesting a specificity of CXCL10 elevation as a function of age. Results of this study, performed in healthy subjects on an age gradient, demonstrate an increase in serum CXCL10 with advancing age overall in females, supporting the hypothesis of enhanced Th1 immune responses in aging.     

Discriminating performance of macular ganglion cell-inner plexiform layer thicknesses at different stages of glaucoma

AIM: To determine the discriminating performance of the macular ganglion cell-inner plexiform layer (GC-IPL) parameters between all the consecutive stages of glaucoma (from healthy to moderate-to-severe glaucoma), and to compare it with the discriminating performances of the peripapillary retinal nerve fiber layer (RNFL) parameters and optic nerve head (ONH) parameters. METHODS: Totally 147 eyes (40 healthy, 40 glaucoma suspects, 40 early glaucoma, and 27 moderate-to-severe glaucoma) of 133 subjects were included. Optical coherence tomography (OCT) was obtained using Cirrus HD-OCT 5000. The diagnostic performances of GC-IPL, RNFL, and ONH parameters were evaluated by determining the area under the curve (AUC) of the receiver operating characteristics. RESULTS: All GC-IPL parameters discriminated glaucoma suspect patients from subjects with healthy eyes and moderate-to-severe glaucoma from early glaucoma patients (P<0.017, for all). Also, minimum, inferotemporal and inferonasal GC-IPL parameters discriminated early glaucoma patients from glaucoma suspects, whereas no RNFL or ONH parameter could discriminate between the two. The best parameters to discriminate glaucoma suspects from subjects with healthy eyes were superonasal GC-IPL, superior RNFL and average c/d ratio (AUC=0.746, 0.810 and 0.746, respectively). Discriminating performances of all the parameters for early glaucoma vs glaucoma suspect comparison were lower than that of the other consecutive group comparisons, with the best GC-IPL parameters being minimum and inferotemporal (AUC=0.669 and 0.662, respectively). Moreover, minimum GC-IPL, average RNFL, and rim area (AUC=0.900, 0.858, 0.768, respectively) were the best parameters for discriminating moderate-to-severe glaucoma patients from early glaucoma patients. CONCLUSION: GC-IPL parameters can discriminate glaucoma suspect patients from subjects with healthy eyes, and also all the consecutive stages of glaucoma from each other (from glaucoma suspect to moderate-to-severe glaucoma). Further, the discriminating performance of GC-IPL thicknesses is comparable to that.     

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

Methods

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

Results

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

Conclusion

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.