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1. Repurposing Atovaquone as a Therapeutic against Acute Myeloid Leukemia (AML): Combination with Conventional Chemotherapy Is Feasible and Well Tolerated

   https://doi.org/10.3390/cancers15041344  

   Stevens, Alexandra McLean; Schafer, Eric S.; Li, Minhua; Terrell, Maci; Rashid, Raushan; Paek, Hana; Bernhardt, Melanie B.; Weisnicht, Allison; Smith, Wesley T.; Keogh, Noah J.; et al (February 2023, Cancers)

   Survival of pediatric AML remains poor despite maximized myelosuppressive therapy. The pneumocystis jiroveci pneumonia (PJP)-treating medication atovaquone (AQ) suppresses oxidative phosphorylation (OXPHOS) and reduces AML burden in patient-derived xenograft (PDX) mouse models, making it an ideal concomitant AML therapy. Poor palatability and limited product formulations have historically limited routine use of AQ in pediatric AML patients. Patients with de novo AML were enrolled at two hospitals. Daily AQ at established PJP dosing was combined with standard AML therapy, based on the Medical Research Council backbone. AQ compliance, adverse events (AEs), ease of administration score (scale: 1 (very difficult)-5 (very easy)) and blood/marrow pharmacokinetics (PK) were collected during Induction 1. Correlative studies assessed AQ-induced apoptosis and effects on OXPHOS. PDX models were treated with AQ. A total of 26 patients enrolled (ages 7.2 months–19.7 years, median 12 years); 24 were evaluable. A total of 14 (58%) and 19 (79%) evaluable patients achieved plasma concentrations above the known anti-leukemia concentration (>10 µM) by day 11 and at the end of Induction, respectively. Seven (29%) patients achieved adequate concentrations for PJP prophylaxis (>40 µM). Mean ease of administration score was 3.8. Correlative studies with AQ in patient samples demonstrated robust apoptosis, OXPHOS suppression, and prolonged survival in PDX models. Combining AQ with chemotherapy for AML appears feasible and safe in pediatric patients during Induction 1 and shows single-agent anti-leukemic effects in PDX models. AQ appears to be an ideal concomitant AML therapeutic but may require intra-patient dose adjustment to achieve concentrations sufficient for PJP prophylaxis. 

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2. The Many Faces of Adversarial Machine Learning

   Vorobeychik, Yevgeniy (January 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Adversarial machine learning (AML) research is concerned with robustness of machine learning models and algorithms to malicious tampering. Originating at the intersection between machine learning and cybersecurity, AML has come to have broader research appeal, stretching traditional notions of security to include applications of computer vision, natural language processing, and network science. In addition, the problems of strategic classification, algorithmic recourse, and counterfactual explanations have essentially the same core mathematical structure as AML, despite distinct motivations. I give a simplified overview of the central problems in AML, and then discuss both the security-motivated AML domains, and the problems above unrelated to security. These together span a number of important AI subdisciplines, but can all broadly be viewed as concerned with trustworthy AI. My goal is to clarify both the technical connections among these, as well as the substantive differences, suggesting directions for future research. 

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3. CLEC11A methylation is correlated to AML subtypes and cytogenetic risk factors but not patient demographics

   https://doi.org/10.1371/journal.pone.0300477  

   Swanson, Allyson J.; Rogowski, Victor J.; Bishop, Jacob A.; Walker, Dylan M.; Roxas, Gina M.; Raimondi, Stacey L. (March 2024, PLOS ONE)

   Baysal, Mehmet (Ed.)

   Acute myeloid leukemia (AML) is an aggressive and lethal cancer of the blood, which leads to the death of over 11,000 patients in the United States each year. Research on identifying, characterizing, and treating AML is crucial in the fight against this deadly disease. Recent studies have examined the role of CLEC11A in cancer, including AML. However, there have been conflicting reports related to tumor progression and survival. Because survival is based on a variety of factors, including classification of the tumor, genetic risk factors, and demographics, it is imperative that we determine what role CLEC11A may have in cancer survival. Therefore, utilizing data from the Genomic Data Commons, we analyzed CLEC11A methylation in 108 AML patients compared to FAB classification, cytogenetic risk factors, age, race, and gender. Our results show statistically significant correlations between methylation of CLEC11A and FAB classification as well as poor genetic risk factors. However, no difference was observed in CLEC11A methylation when compared to demographic data. Our results, matched with a known biological function of CLEC11A in early hematopoiesis, indicate that CLEC11A may be an important marker for AML diagnosis and prognosis and provide relevant data in the ongoing search for novel therapeutics to improve AML survival. 

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4. SoK: Money Laundering in Cryptocurrencies

   https://doi.org/10.1145/3465481.3465774  

   Kolachala, Kartick; Simsek, Ecem; Ababneh, Mohammed; Vishwanathan, Roopa (August 2021, ARES 2021: The 16th International Conference on Availability, Reliability and Security)

   null (Ed.)

   Money laundering using cryptocurrencies has become increasingly prevalent, and global and national regulatory authorities have announced plans to implement stringent anti-money laundering regulations. In this paper, we examine current anti-money laundering (AML) mechanisms in cryptocurrencies and payment networks from a technical and policy perspective, and point out practical challenges in implementing and enforcing them. We first discuss blacklisting, a recently proposed technique to combat money laundering, which seems appealing, but leaves several unanswered questions and challenges with regard to its enforcement. We then discuss payment networks and find that there are unique problems in the payment network domain that might require custom-designed AML solutions, as opposed to general cryptocurrency AML techniques. Finally, we examine the regulatory guidelines and recommendations as laid out by the global Financial Action Task Force (FATF), and the U.S. based Financial Crimes Enforcement Network (FinCEN), and find that there are several ambiguities in their interpretation and implementation. To quantify the effects of money laundering, we conduct experiments on real-world transaction datasets. Our goal in this paper is to survey the landscape of existing AML mechanisms, and focus the attention of the research community on this issue. Our findings indicate the community must endeavor to treat AML regulations and technical methods as an integral part of the systems they build and must strive to design solutions from the ground up that respect AML regulatory frameworks. We hope that this paper will serve as a point of reference for researchers that wish to build systems with AML mechanisms, and will help them understand the challenges that lie ahead. 

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5. Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets

   https://doi.org/10.3390/jpm11030193  

   Wooten, David J.; Gebru, Melat; Wang, Hong-Gang; Albert, Réka (March 2021, Journal of Personalized Medicine)

   null (Ed.)

   FLT3-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with FLT3 inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in FLT3-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication FLT3 mutation (FLT3-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of FLT3-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence. 

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