Introduction / Purpose: Retinoblastoma is the most common intra-ocular tumour of childhood. Radiotherapy and chemotherapy has improved survival for the disease over the last 40 years. Looking for prognostic criteria is one of the major research areas that inspires disease management teams all-over the world. Many protocols were designed over the years to correlate the presence of certain pathological and/or molecular features and prognosis of the disease. Children’s Cancer Hospital Egypt 57357 (CCHE) is a new cancer center in Egypt that serves about 25-40% of the pediatric oncology population in the country. The aim of this study is to present the histopathological findings in Egypt leading pediatric oncology center and impact of implementing CAP protocol on the improvement of patient care. Materials & Methods: CCHE Retinoblastoma study team implemented College of American Pathologist (CAP) protocol of Retinoblastoma version 3.0 (Based on AJCC/UICC TNM, 7th edition) for examining enucleation specimens. Pathology team has conducted an internal training for pathologists. New electronic case report forms (eCRFs) were designed for reporting the progress. Real-time online statistical analysis system was integrated with eCRFs. Implementation included prospective processing and data entry of new specimens properties beside revising of all specimens prior to the implementation of the protocol. Healthcare quality measures included required time for releasing the reports. Results: Between July 2007 and May 2013 we’ve examined 198 enucleation specimens. Mean of all ocular diameters was 22.2 mm. Mean Optic nerve stump length was 6.35 mm. Invaded structures included Cornea (5.3%), Anterior chamber (15.4%), Iris (8.3%), Lens (5.9%), Ciliary body (18.3%), Choroid, (72.2%), Sclera (17.2%), and Orbital tissue (7.1%). Any degree of Optic Nerve invasion was noted in 51% of specimens while surgical margin was involved in 10.8%. Validation rules in eCRFs helped the team to complete missing data and provide systematic reporting. Integrated online analysis system provided a real-time reporting facility for detecting protocol implementation progress. Conclusions / Significance: Implementing CAP retinoblastoma protocol resembled a roadmap for improving patients’ care. It supported the systematic reporting of cases, decreasing errors and missing data and provided research informatics team with a blueprint for developing a real-time monitoring system. This system can be integrated in the practice of emerging teams to ensure adherence to quality measures.

OBJECTIVES: The aim of this work was to explore the value of serum vascular endothelial growth factor-A (VEGF-A) in patients with metastatic triple negative breast cancer (TNBC) treated with chemotherapy. The primary end point was overall survival (OS). Secondary end points were response rate (RR), progression-free survival (PFS) and VEGF-A level at baseline, mid-therapy and at the end of therapy.

DESIGN AND METHODS: Female patients aged 18 years or above with histologically proven metastatic TNBC were included. Serum VEFG-A levels were measured at baseline, after the 3rd and 6th cycles of FAC chemotherapy regimen (Fluorourcil, Adriamycin, and Cyclophamide).

RESULTS: The overall RR was 57%. The median PFS and OS were 7 and 11.2 months, respectively (95% CI: 4.3-9.7 and 3.8-18.5 months, respectively). Patients whose disease progressed despite therapy had a significantly higher baseline VEGF-A level than those who did not progress. VEGF-A level did not drop with continuation of therapy. Patients with high VEGF-A level had a significantly lower PFS but not OS than patients with low levels.

CONCLUSION: The outcome of metastatic TNBC is poor with FAC chemotherapy regimen. Alternative chemotherapeutic regimens and novel therapeutic approaches including targeting of VEGF and/or its receptors are warranted.

The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.

Nanomedicine is a rapidly emerging field with several breakthroughs in the therapeutic drug delivery application. The unique properties of the nanoscale delivery systems offer huge advantages to their payload such as solubilization, increased bioavailability, and improved pharmacokinetics with an overall goal of enhanced therapeutic index. Nanomedicine has the potential for integrating and enabling new therapeutic modalities. Several nanoparticle-based drug delivery systems have been granted approval for clinical use based on their outstanding clinical outcomes. Nanomedicine faces several challenges that hinder the realization of its full potential. In this review, we discuss the critical formulation- and biological-related quality features that significantly influence the performance of nanoparticulate systems in vivo. We also discuss the quality-by-design approach in the pharmaceutical manufacturing and its implementation in the nanomedicine. A deep understanding of these nanomedicine quality checkpoints and a systematic design that takes them into consideration will hopefully expedite the clinical translation process.