Day 2 :
Universidade do Minho, Portugal
Manuel F M Costa holds a PhD degree in Science (Physics) from the University of Minho (Portugal) where he works since 1985 at Physics Department teaching and performing applied research in optical metrology, applied optics, thin films and nanoscience, optometry and science education and literacy. He presented over 300 communications in international meetings and published around the same number of scientific papers, monographs and books; editor or Member of the Editorial Board of several scientific and educational international journals. He is acting as Chairperson on 19 international conferences; Member of the Scientific Advisory Board of EOS, Member of the Board of the IberoAmerican Optics Network and Member of the Board of Stakeholders of PHOTONICS’21. He is President of the Hands-on Science Network, of the Portuguese Territorial Committee of the International Commission for Optics and of the Portuguese Society for Optics and Photonics, SPOF and Senior Member of SPIE and Fellow of European Optical Society.
Neural networks are successfully being used for many years in a large number of fields of science and technology also in medicine and virtually all fields of knowledge. Even in simple non advanced ways the results of the application of neural networks to a number of problems give good and reliable results comparable and most often better than traditional methods. In the work herein we report an application to an optometry problem: the automated classification of digital photorefraction images was obtained to characterize the refractive status of patients, mostly young children. The importance of an early evaluation of the condition of the visual system of infants is long time recognized. Non corrected optometric or ophthalmologic problems may lead to major vision and developmental non-reversible limitations in the future. Among the objective methods of refraction photorefractive techniques are specifically designed for screening young children. Over the years a number of photorefraction systems with different grades of complexity and automation were developed. One critical problem that needs to deal with in any approach of these systems is the interpretation and classification of the photorefraction images.
- Image Analysis
SigTuple Technologies Pvt Ltd, India
Title: Study on the performance of an artificial intelligence system for image based analysis of urine samples
Time : 11:05-11:40
Renu Ethirajan has completed her MBBS and DNB Pathology from Father Muller Medical College, Mangalore, India. She is currently working as Director Pathology for SigTuple, an organization that provides healthcare solutions driven by artificial intelligence and image processing. She has worked as a Consultant Hemato-Oncopathologist and has reported flowcytometry for more than 8 years at HCG Cancer Hospital, Bangalore. She is also trained in molecular diagnosis like fluoroscent in-situ hybradization and immuno-hematology. She has presented multiple papers in reputed CMEs and conferences. She has participated at the National Indian Conclave as a panelist on artificial intelligence.
In this study, we evaluate the performance of Shrava, a cloud based artificial intelligence (AI) system for automated analysis of images captured from urine samples. Identification and morphological classification of objects in urine sediments by Shrava was compared with the results from Sysmex UF-1000i urine analyzer and manual microscopy. Thirty urine samples were analysed for the study, wherein, on an average, 50 different fields of views were captured at a magnification of 400x from slides prepared from the samples. Classification of objects from the captured images was verified by three qualified medical experts and sensitivity, specificity, and accuracy of the classification results were calculated. Classification performance of Shrava was evaluated for RBCs, WBCs, crystals, epithelial cells and organisms (yeast and bacteria). The specificity for classification was above 97% for RBCs and above 99% for all other objects, while sensitivity was above 99% for yeast and epithelial cells, above 97% for RBCs, WBCs, and bacteria, and above 87% for crystals. Overall, classification accuracy for all objects was 96.4%. We also evaluated the sensitivity of Shrava for the above mentioned objects vis-a-vis reports obtained through a combination of urine analyser and manual microscopy and it was found to be 96.19%. Shrava was found to be effective in identifying and classifying objects in urine sediments. It saves time by aiding pathologists as a screening solution and also accelerates the turnaround time, thereby, increasing the productivity of pathologists and the laboratory.
Chughtai lab, Lahore, Pakistan
Time : 11:40-12:15
Anila Chughtai has completed her Bachelor of Medicine & Bachelor of Surgery degrees (MBBS) from Services Institute of Medical Sciences, Lahore. Currently, she is working as a second year Post-graduate trainee at Chughtai lab Lahore.
Malignant triton tumor (MTT) is a tumor arising from Schwann cells with divergent rhabdomyoblastic differentiation. It is a rare and aggressive variant of malignant peripheral nerve sheath tumor (MPNST). We present a case of 33 years old male with thigh swelling. Surgical excision was done followed by histopathological and immunohistochemical (IHC) workup which revealed a tumor showing two types of cell populations including pleomorphic spindle cells and large cells with pleomorphic eccentric nuclei. Spindle cells showed positivity for S-100 IHC stain confirming their neural origin while large cells with eccentric nuclei showed positivity for desmin & myogenin IHC stains confirming their rhabdomyoblastic origin. Hence, the diagnosis of MTT was made. A 5 years survival rate for MTT is 5-15% compared to 50-60% for MPNST. Considering it is a rare entity with aggressive clinical behavior and poor prognosis, correct diagnosis is essential that can be achieved by careful histological and IHC evaluation.
Tehran University of Medical Sciences, Tehran, Iran
Time : 12:15-12:50
Armin Ai is a Dentestry student at Tehran University of Medical Sciences.He has published more than 12 papers in reputed journals.
Glioblastoma multiform (GBM) is the most aggressive glial neoplasm. Absolutely, the survival, growth, and invasion of GBM cells are promoted by various inflammatory cytokines. Statins, such as atorvastatin, are known to exert anti-inflammatory effects. Chronic inflammation is a pathological feature of cancer. Growth of solid tumors results in most cases in a hypoxic microenvironment and the release of various cytokines and growth factors, which together increase inflammation, angiogenesis in tumor stroma, and triggering signaling cascades that activate NFkappa B and STAT3 that produces predominantly by a specific subset of T helper cells (Th cells), namely Th17 cells. Interleukin-17 (IL-17) has emerged as a central player in the mammalian immune system. IL-17RA is expressed in most tissues examined to activate many of the same signaling cascades as innate cytokines such as TNFα and IL-1β. Furthermore, emerging knowledge regarding IL-17A/IL-17RA signaling in numerous tissues suggests an important role in health and disease beyond the immune system. This increasing evidence suggests that IL-17A and Th17 play a main role in autoimmune inflammation. A VEGF independent pathway was also found via NF-κB, which leads to suppression of the immune response targeting cancer cell. In this study, we investigated the anti-inflammatory and anti-angiogenesis activity of atorvastatin on engineered three-dimensional (3D) human tumor models using glioma spheroids and Human Umbilical Vein Endothelial Cells (HUVECs) in fibrin gel as tumor models in different concentrations of atorvastatin (1, 5, 10 µM). After 48 hours exposing with different concentrations of atorvastatin, cell migration of HUVECs were investigated. After 24 and 48 hours exposing with atorvastatin VEGF, CD31, IL-17R genes expression by real time PCR were assayed. In the current study, results have demonstrated a potential impact of IL-17R in glioma growth and progression. The results showed that atorvastatin has potent anti- inflammatory and anti-angiogenic effect against glioma spheroids by downregulates IL-17RA and VEGF expression especially at 10 μM concentration. The most likely mechanisms are the inhibition of inflammation by IL-17RA interaction with NFKB signaling pathway. Finally, these results suggest that this biomimetic model with fibrin may provide a vastly applicable 3D culture system to study the effect of anti-cancer drugs such as atorvastatin on tumor malignancy in vitro and in vivo and atorvastatin could be used as agent for glioblastoma treatment.