ROLE OF AUTOPHAGY IN THE RESPONSE OF HS578T BREAST TUMOR CELLS TO RADIATION

Breast cancer is the most commonly observed cancer type in women and is the second leading cause of cancer death in women. Radiation can be used to debulk tumors prior to surgery as well as to treat patients after surgery and/or chemotherapy. Previous studies from our laboratory have shown that the anti –malarial drug chloroquine sensitizes breast cancer cell lines to radiation by suppression of autophagy which is a conservative catabolic process that can be cytoprotective. The scientific literature has demonstrated that many tumor cell systems undergo cytoprotective autophagy and that pharmacological or genetic inhibition of autophagy leads to other modes of cell death such as apoptosis. Acridine orange staining was used for determination of acidic vacuole formation, an indication of autophagy and DAPI/TUNEL staining was used to identify apoptotic cells. Our studies in Hs578t breast tumor cells show the lack of sensitization by chloroquine upon autophagy inhibition with minimal apoptosis when cells are treated with 5 × 2Gy radiation. The extent of apoptosis was not increased upon autophagy inhibition by Chloroquine as determined by DAPI/TUNEL assays and quantified by Flow Cytometry using AnnexinV/PI. The potential role of senescence in the effects of radiation in the Hs578t cells was determined with the use of β-Galactosidase dye staining for senescence. It appears from these studies that autophagy need not to be cytoprotective in all breast cancer cell lines. Additional studies are in progress to effort to identify the factors that might distinguish between cytoprotective and non-cytoprotective autophagy

Picking on the family: disrupting android malware triage by forcing misclassification

Machine learning classification algorithms are widely applied to different malware analysis problems because of their proven abilities to learn from examples and perform relatively well with little human input. Use cases include the labelling of malicious samples according to families during triage of suspected malware. However, automated algorithms are vulnerable to attacks. An attacker could carefully manipulate the sample to force the algorithm to produce a particular output. In this paper we discuss one such attack on Android malware classifiers. We design and implement a prototype tool, called IagoDroid, that takes as input a malware sample and a target family, and modifies the sample to cause it to be classified as belonging to this family while preserving its original semantics. Our technique relies on a search process that generates variants of the original sample without modifying their semantics. We tested IagoDroid against RevealDroid, a recent, open source, Android malware classifier based on a variety of static features. IagoDroid successfully forces misclassification for 28 of the 29 representative malware families present in the DREBIN dataset. Remarkably, it does so by modifying just a single feature of the original malware. On average, it finds the first evasive sample in the first search iteration, and converges to a 100% evasive population within 4 iterations. Finally, we introduce RevealDroid*, a more robust classifier that implements several techniques proposed in other adversarial learning domains. Our experiments suggest that RevealDroid* can correctly detect up to 99% of the variants generated by IagoDroid

AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection

© 2016 Elsevier Ltd The wide popularity of Android systems has been accompanied by increase in the number of malware targeting these systems. This is largely due to the open nature of the Android framework that facilitates the incorporation of third-party applications running on top of any Android device. Inter-process communication is one of the most notable features of the Android framework as it allows the reuse of components across process boundaries. This mechanism is used as gateway to access different sensitive services in the Android framework. In the Android platform, this communication system is usually driven by a late runtime binding messaging object known as Intent. In this paper, we evaluate the effectiveness of Android Intents (explicit and implicit) as a distinguishing feature for identifying malicious applications. We show that Intents are semantically rich features that are able to encode the intentions of malware when compared to other well-studied features such as permissions. We also argue that this type of feature is not the ultimate solution. It should be used in conjunction with other known features. We conducted experiments using a dataset containing 7406 applications that comprise 1846 clean and 5560 infected applications. The results show detection rate of 91% using Android Intent against 83% using Android permission. Additionally, experiment on combination of both features results in detection rate of 95.5%

Nodular rheumatoid arthritis resembling gout

A 57-year-old man presented for review in the rheumatology outpatients clinic. He had a 30-year history of rheumatoid arthritis and was known to be positive for rheumatoid factor and anticitrullinated protein antibodies (anti-CCP antibodies). He had been treated over the past 30 years with various immunosuppression regimens, including long-term methotrexate, leflunomide and sulfasalazine. His disease had been stable for the past 2 years with no recent increase in nodules or episodes of acute synovitis. Several times in the past 15 years his diagnosis of rheumatoid arthritis had come into question due to the atypical appearance of his hands which closely resembled gout (figures 1 and 2). On two occasions, excision of rheumatoid nodules was sent for histology to exclude other diagnoses such as gout and multicentric reticulohistiocytosis