Correction to: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (Genetics in Medicine, (2020), 22, 8, (1391-1400), 10.1038/s41436-020-0812-7)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

A combinatorial theorem for a class of residual treatment effects designs

In this paper a combinatorial theorem for a class of residual treatment effects designs is proved. It is assumed that the number of treatments of a design in this class is a prime or a prime power. A method of constructions of the class of designs under consideration is given in detail. The theorem presented here gives complete information on the frequencies of occurrences of ordered pairs of treatments among various types of ordered pairs of treatments arising from the sequences of the design. The proof of the theorem is based on the properties of symmetrically repeated difference sets. © 1993

A combinatorial theorem for a class of residual treatment effects designs

In this paper a combinatorial theorem for a class of residual treatment effects designs is proved. It is assumed that the number of treatments of a design in this class is a prime or a prime power. A method of constructions of the class of designs under consideration is given in detail. The theorem presented here gives complete information on the frequencies of occurrences of ordered pairs of treatments among various types of ordered pairs of treatments arising from the sequences of the design. The proof of the theorem is based on the properties of symmetrically repeated difference sets.Combinatorially balanced design difference sets residual treatment effects Galios field

Exosomes: Composition, biogenesis, and mechanisms in cancer metastasis and drug resistance

Tumor-derived exosomes (TDEs) participate in formation and progression of different cancer processes, including tumor microenvironment (TME) remodeling, angiogenesis, invasion, metastasis and drug-resistance. Exosomes initiate or suppress various signaling pathways in the recipient cells via transmitting heterogeneous cargoes. In this review we discuss exosome biogenesis, exosome mediated metastasis and chemoresistance. Furthermore, tumor derived exosomes role in tumor microenvironment remodeling, and angiogenesis is reviewed. Also, exosome induction of epithelial mesenchymal transition (EMT) is highlighted. More importantly, we discuss extensively how exosomes regulate drug resistance in several cancers. Thus, understanding exosome biogenesis, their contents and the molecular mechanisms and signaling pathways that are responsible for metastasis and drug-resistance mediated by TDEs may help to devise novel therapeutic approaches for cancer progression particularly to overcome therapy-resistance and preventing metastasis as major factors of cancer mortality

Generation of Induced Pluripotent Cancer Cells from Glioblastoma Multiform Cell Lines

Generation of induced pluripotent stem cells (iPSCs) has been described as a powerful method to dedifferentiate the specialized cells to pluripotency. However, obtaining cancer-specific iPS cells (iPCs) encounters several barriers. The generation of iPCs provides valuable experimental platforms to mimic oncogenesis and offers potentials regarding drug screening. To overcome the difficulties regarding the iPC generation, we aimed at optimizing the generation of iPCs from glioblastoma multiform (GBM) cell lines and at understanding the potential barriers ahead of this process. The T731, T653, and mouse embryonic fibroblast cells were transduced by using retroviral plasmids encoding Oct4, Sox2, and Klf4. The cells were cultured on a layer of feeder cells for 14 days in iPS media and the obtained colonies were then picked and expanded to be evaluated for pluripotency markers by alkaline phosphatase staining, qRT-PCR, and Western blotting. Our findings confirmed resistance in cancer cells to achieve the pluripotency markers. In addition to designing technical tricks to obviate the barriers ahead of iPC generation, we suggested the small molecule PD98059 to enhance the efficiency of iPC generation from GBM cell lines. The resulting iPCs can further be used as a platform to study the mechanism of cancer formation and as a tool for drug screening for the treatment of patients with GB

Non-coding RNAs underlying chemoresistance in gastric cancer.

BackgroundGastric cancer (GC) is a major health issue in the Western world. Current clinical imperatives for this disease include the identification of more effective biomarkers to detect GC at early stages and enhance the prevention and treatment of metastatic and chemoresistant GC. The advent of non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long-non coding RNAs (lncRNAs), has led to a better understanding of the mechanisms by which GC cells acquire features of therapy resistance. ncRNAs play critical roles in normal physiology, but their dysregulation has been detected in a variety of cancers, including GC. A subset of ncRNAs is GC-specific, implying their potential application as biomarkers and/or therapeutic targets. Hence, evaluating the specific functions of ncRNAs will help to expand novel treatment options for GC.ConclusionsIn this review, we summarize some of the well-known ncRNAs that play a role in the development and progression of GC. We also review the application of such ncRNAs in clinical diagnostics and trials as potential biomarkers. Obviously, a deeper understanding of the biology and function of ncRNAs underlying chemoresistance can broaden horizons toward the development of personalized therapy against GC

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

PURPOSE: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments. METHODS: We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data. RESULTS: Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs. CONCLUSION: Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase