Modulated anti-VEGF therapy under the influence of lipid metabolizing proteins in Age related macular degeneration: a pilot study

Age-related macular degeneration (AMD) is a devastating retinal disease that results in irreversible vision loss in the aged population. The complex genetic nature and degree of genetic penetrance require a redefinition of the current therapeutic strategy for AMD. We aimed to investigate the role of modifiers for current anti-VEGF therapy especially for non-responder AMD patients. We recruited 78 wet AMD cases (out of 278 AMD patients) with their socio-demographic and treatment regimen. Serum protein levels were estimated by ELISA in AMD patients. Data pertaining to the number of anti-VEGF injections given (in 1 year) along with clinical images (FFA and OCT) of AMD patients were also included. Visual acuity data (logMAR) for 46 wet AMD cases out of a total of 78 patients were also retrieved to examine the response of anti-VEGF injections in wet AMD cases. Lipid metabolizing genes (LIPC and APOE) have been identified as chief biomarkers for anti-VEGF response in AMD patients. Both genotypes ‘CC’ and ‘GC’ of LIPC have found to be associated with a number of anti-VEGF injections in AMD patients which could influence the expression of B3GALTL,HTRA1, IER3, LIPC and SLC16A8 proteins in patients bearing both genotypes as compared to reference genotype. Elevated levels of APOE were also observed in group 2 wet AMD patients as compared to group 1 suggesting the significance of APOE levels in anti-VEGF response. The genotype of B3GALTL has also been shown to have a significant association with the number of anti-VEGF injections. Moreover, visual acuity of group 1 (≤ 4 anti-VEGF injections/year) AMD patients was found significantly improved after 3 doses of anti-VEGF injections and maintained longitudinally as compared to groups 2 and 3. Lipid metabolising genes may impact the outcome of anti-VEGF AMD treatment.

Coronary Artery Stenting Affects Wall Shear Stress Topological Skeleton

Despite the important advancements in the stent technology for the treatment of diseased coronary arteries, major complications still affect the post-operative long-term outcome. The stent-induced flow disturbances, and especially the altered wall shear stress (WSS) profile at the strut level, play an important role in the pathophysiological mechanisms leading to stent thrombosis (ST) and in-stent restenosis (ISR). In this context, the analysis of the WSS topological skeleton is gaining more and more interest by extending the current understanding of the association between local hemodynamics and vascular diseases. The present study aims to analyze the impact that a deployed coronary stent has on the WSS topological skeleton. Computational fluid dynamics simulations were performed in three stented human coronary artery geometries reconstructed from clinical images. The selected cases presented stents with different designs (i.e., two contemporary drug eluting stents and one bioresorbable scaffold) and included regions with stent malapposition or overlapping. A recently proposed Eulerian-based approach was applied to analyze the WSS topological skeleton features. The results highlighted that the presence of single or multiple stents within a coronary artery markedly impacts the WSS topological skeleton. In particular, repetitive patterns of WSS divergence were observed at the luminal surface, highlighting a WSS contraction action proximal to the struts and a WSS expansion action distal to the struts. This WSS action pattern was independent from the stent design. In conclusions, these findings could contribute to a deeper understanding of the hemodynamic-driven processes underlying ST and ISR.

Design and Development of AI based Approach for Histopathology Cancer Screening and Identification

Cancer is a heterogeneous disorder comprising various types and sub-types. Early detection, screening, and diagnosis of cancer types are necessary for facilitating cancer research in early diagnosis, management, and the evolution of successful therapies. Existing methodologies were only able to classify and diagnose a single variety of cancer based on a homogeneous dataset but more focused on predicting patient survivability then cure. This research defines a machine learning-based methodology to develop an universal approach in diagnosis, detection, symptoms-based prediction, and screening of histopathology cancer, their types, and sub types using a heterogeneous dataset based on images and scans. In this architecture, we use VGG-19 based 3D-Convolutional Neural Network for deep feature extraction and later perform regression using a random forest algorithm. We create a heterogeneous dataset consisting of results from laboratory tests, imaging tests and biopsy reports, not only relying on clinical images. Initially, we categorize tumors and lesions as benign or malignant and classify the malignant lesions into their sub-types, detecting their severity and growth rate. Our system is designed to predict risk at multiple time-points, leverage optional risk factors if they are available and produce predictions that are consistent across mammography machines. We found the classification accuracy for categorizing tumors as cancerous to be 95% whereas the accuracy for classification of malignant lesions into their sub-types to be 94%..

Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

The use of digital dental photography in an Eastern European country

Aims. To assess the use of digital photography in dentistry and its relation with the professional experience of the dental practitioners in Romania. Methods. An anonymous questionnaire including eight questions was distributed online to collect information on the use of dental photography. Results. 84.84% of the respondents were using a photographic equipment in their clinical practice. Regarding the type of photographic equipment used, 51.79% of the participants indicated DSLR cameras, 44.05% smartphones, 2.38% compact cameras and 1.78% other devices for taking clinical images. There was a significant association (p<0.05) between the experience of the practitioners and the use of dental photography, type of equipment and protocol used. Conclusions. Respondents with more than 10 years of experience were more likely to use digital photography in their practice than those with less experience. Most of the digital photography users with more than 10 years of experience were taking images with a DSLR Camera (65.52%) followed by 31.04% smartphone users. Conversely, 56.42% of the clinical photography users with less than 5 years of experience mainly preferred a smartphone device and 41.02% a DSLR Camera.