Gozzi Michela, born in 1973, obtained a degree in 1997 from the Faculty of Medicine and Surgery of Brescia in a Biomedical laboratory techniques with a score of 110/110 with honors. From 1997 to 2005 she worked at the Service of Pathological Anatomy of the Poliambulanza Foundation, then she worked as co-ordinator of the Cytology service of the OxigenLab medical analysis laboratory. From 2014 she work as a freelance cytologist in the cytopathology service of the Clinical Institute of the City of Brescia.
In case of rhinitis the diagnosis requires a precise diagnostic procedure and is essentially based on the esclusion of allergies, infections or defects of the nose. The clinical diffusion of nasal cytology has allowed to recognize the non-allergic rhinitis. NASAL Cytology allows to identify non-allergic cell-mediated rhinitis from neutrophilis (N.A.R.N.E), eosinophils (N.A.R.E.S), and mast cells (N.A.R.M.A). The our enrichment technique in the liquid phase also allows to improve the quality of the sample making it easier to read by the pathologist. The poster describes the technique used to set up the samples and the result obtained.
Vivian Rouston was born in 1979. She obtained her MBBCh in 2004 and Masters in Pathology in 2015 from Faculty of Medicine Alexandria University. She was trained for histopathology and cytopathology at histopathology division of the Department of Pathology, St James\'s university hospital, the Leeds Teaching Hospitals, NHS Trust, United Kingdom. She is working as a histopathology specialist in a general hospital in Egypt.
Vivian Gaber Daboos Rouston(Abstract)
Background: Bladder cancer even in early stage develop recurrence. Poor sensitivity of cytology & invasiveness of urethrocystoscopy have generated interest in non-invasive tools to monitor for recurrence. Caspase-3 & survivin have central role in regulation of apoptosis. Survivin can aid early diagnosis, determine prognosis in multiple cancer types & predict response to anti-cancer therapies. Its combination with other biomarkers as caspase-3 enhance prognostication & prediction of treatment response in UBC.Methods: Immunohistochemical expression of survivin & caspase-3 were assessed in 44 Egyptian consecutive patients with UBC & 7 cystoscopic biopsies of cystitis as control reactive benign urothelium. Relationships between their expression, clinicopathological characteristics, diagnostic & prognostic performance were statistically analyzed.Findings: No survivin immunoreactivity was identified in non-neoplastic bladder tissue. Expression of survivin & caspase-3 was altered in 42(95.5%) & 10(22.7%) cases, respectively. There was statistically significant moderate positive correlation between survivin & caspase-3 expression among whole studied cases (p=.006). Expression of either survivin or caspase-3 protein individually significantly differ (p=0.000) in cancer status from control cases. Survivin was an independent predictor of UBC in multivariable analyses. Diagnostic accuracy of survivin alone was significantly better than caspase-3 alone (sensitivity 81.82% Vs 68.18%, p=.027). Addition of survivin immunoreactivity to model including caspase-3 expression improved diagnostic accuracy with a sensitivity of 93.18%. Addition of gender to the previous model improved more diagnostic accuracy with sensitivity of 100%.Interpretation: Survivin alone is very promising marker & reliable indicator in UBC. Survivin & caspase-3 antigens have a cooperative effect on bladder cancer, their simultaneous evaluation augments diagnostic sensitivity.Abbreviations: UBC, Urinary Bladder Cancer or Carcinoma; TURBT, Trans Uretheral Resection of Bladder Tumor
Weidong XIE is a inventor and the founder and CEO of DM Intelligence. Following graduation from Imperial College London with honor in biological medicine he took office as associate professor in Sun Yat-sen University and director/PI in St. Jude Childrenâ€˜s Research Hospital, USA. His research results in regards to T-cell viral immunity were listed as the remarkable scientific breakthroughs by famous journals.rnAfter a decade of experience in small molecule drugs discovery, he leads technology startups successfully and AI in medical imaging & pathology diagnosis is the key point he focuses on.
Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. The diagnosis of biopsy tissue with hematoxylin and eosin (H&E) stained images is non-trivial and specialists often disagree on the final diagnosis. Actually, Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Therefore, we have established a diagnostic tool based on a deep-learning framework for the screening of patients with invasive ductal carcinoma. The dataset of tissue slides used in this project consists of 30,000 samples from eligible patients in our hospital. Available tissue samples above were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. An accuracy of 94% was obtained for non-cancer (i.e. normal or benign) vs. malignant (i.e. invasive carcinoma). This will be helping specialists identify cancerization which is not visible under a single microscope, and this is just the start of what we have planned.