Cystatin C and lactoferrin concentrations in biological fluids as possible prognostic factors in eye tumor development

Objectives. To investigate the possible role of cystatin C in eye biological fluids locally and in serum and lactoferrin revealing anti-tumor activity in eye tumor development. Background. The increased number of eye tumors was registered recently not only in the countries with high insolation, but also in the northern countries including Russia (11 cases per million of population). Search for new biological markers is important for diagnosis and prognosis in eye tumors. Cystatin C, an endogenous inhibitor of cysteine proteases, plays an important protective role in several tumors. Lactoferrin was shown to express anti-tumor and antiviral activities. It was hypothesized that cystatin C and lactoferrin could serve as possible biomarkers in the diagnosis of malignant and benign eye tumors. Study design. A total of 54 patients with choroidal melanoma and benign eye tumors were examined (part of them undergoing surgical treatment). Serum, tear fluid and intraocular fluid samples obtained from the anterior chamber of eyes in patients with choroidal melanoma were studied. Methods. Cystatin C concentration in serum and eye biological fluids was measured by commercial ELISA kits for human (BioVendor, Czechia); lactoferrin concentration – by Lactoferrin-strip D 4106 ELISA test systems (Vector-BEST, Novosibirsk Region, Russia). Results. Cystatin C concentration in serum of healthy persons was significantly higher as compared to tear and intraocular fluids. In patients with choroidal melanoma, increased cystatin C concentration was similar in tear fluid of both the eyes. Lactoferrin level in tear fluid of healthy persons was significantly higher than its serum level. Significantly increased lactoferrin concentration in tear fluid was noted in patients with benign and malignant eye tumors. Conclusion. Increased level of cystatin C in tear fluid seems to be a possible diagnostic factor in the eye tumors studied. However, it does not allow us to differentiate between malignant and benign eye tumors. Similar changes were noted for lactoferrin in tear fluid

Adult rabbit retinal transplants

PURPOSE: To study the survival of adult retinal grafts prepared in a physiologically optimized way.METHODS: Twenty-three rabbits received an adult full-thickness rabbit retinal transplant positioned under the host retina, using a vitrectomy technique. The transplants were prepared using a procedure based on a previously described in vitro model used for physiological experiments on the adult retina. Five rabbits received a fragmented graft. All grafts were prelabeled with 4',6-diaminidin-2-phenylindoldihydrochloride (DAPI) to allow identification. The eyes were examined by light and fluorescence microscopy 6 to 174 days after surgery. To assess the amount of cell death in the graft before actual transplantation, in vitro experiments were performed. The extent of cell death in retinas prepared by the optimized protocol was examined and compared with a simpler preparation previously used successfully for embryonic grafts. The amount of cell death in the in vitro experiments was evaluated using a fluorescent green nucleic acid stain that penetrates dying cells.RESULTS: In 21 of the 23 animals that received full-thickness grafts prepared in an optimized way, the transplant survived. Sixteen grafts, including all four with a 174-day survival time, displayed normal morphology, with all retinal layers preserved. The fragmented grafts survived poorly. The in vitro experiments showed minimal cell death in retinas prepared according to the optimized protocol, whereas control retinas displayed extensive cell death after 5 hours.CONCLUSIONS: The results showed that it is possible to transplant adult retina in the rabbit and that the grafts survive well if they are prepared under physiologically optimized conditions and the integrity of the grafted tissue is kept intact

Deep learning volumetric brain segmentation based on spectral CT

The purpose of this pilot study was to evaluate if a deep learning network can be used for brain segmentation of grey and white matter using spectral computed tomography (CT) images. Spectral CT has the advantage of a lower noise level and an increased soft tissue contrast, compared to conventional CT, which should make it better suited for segmentation tasks. Being able to do volumetric assessments on CT, not only magnetic resonance imaging (MRI) would be of great clinical benefit. The training set consisted of two patients and the validation data set of one patient. Included patients had a brain CT from a spectral CT as well as a T1-weighted MRI. MRI was used for an MR-based segmentation using FreeSurfer. A convolutional neural network was trained to identify grey and white matter in virtual monoenergetic images (70 keV) from spectral CT, using the MR-based segmentation as reference, and tested to assess its' performance. The network was able to identify both grey and white matter in roughly the correct areas. In general, there was an overestimation of grey matter. These results motivate further studies, as we predict that the network will be more accurate when trained on a larger data set

Correlations between cholinergic neurons and muscarinic m2 receptors in the rat retina

Acetylcholine is well established as the neurotransmitter of starburst amacrine cells in the vertebrate retina but their function is poorly understood. We compared the distribution of muscarinic m2 receptors in the rat retina with the localization of the starburst cell processes. mAChR2 immunoreactivity appeared in a central band in the inner plexiform layer, which did not co-localize with the processes of the cholinergic amacrine cells. We found co-labelling of VAChT and ChAT making it highly unlikely that there are undetected cholinergic neurons in rat retina. Most mAChR2 receptors were located far from the cholinergic neurons, suggesting that most of them are unlikely to be associated with conventional cholinergic synapses

Table_1_Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.DOCX

IntroductionRadiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.MethodsNon-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.ResultsThe hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value DiscussionAn open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.</p

Image_1_Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.TIFF

IntroductionRadiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.MethodsNon-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.ResultsThe hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value DiscussionAn open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.</p