Publications by authors named "Taha ValizadehAslani"

3 Publications

  • Page 1 of 1

RRM2B Is Frequently Amplified Across Multiple Tumor Types: Implications for DNA Repair, Cellular Survival, and Cancer Therapy.

Front Genet 2021 12;12:628758. Epub 2021 Mar 12.

Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, PA, United States.

plays a crucial role in DNA replication, repair and oxidative stress. While germline mutations have been implicated in mitochondrial disorders, its relevance to cancer has not been established. Here, using TCGA studies, we investigated alterations in cancer. We found that is highly amplified in multiple tumor types, particularly in -amplified tumors, and is associated with increased mRNA expression. We also observed that the chromosomal region 8q22.3-8q24, is amplified in multiple tumors, and includes , along with several other cancer-associated genes. An analysis of genes within this 8q-amplicon showed that cancers that have both -amplified along with have a distinct pattern of amplification compared to cancers that are unaltered or those that have amplifications in or only. Investigation of curated biological interactions revealed that gene products of the amplified 8q22.3-8q24 region have important roles in DNA repair, DNA damage response, oxygen sensing, and apoptosis pathways and interact functionally. Notably, -amplified cancers are characterized by mutation signatures of defective DNA repair and oxidative stress, and at least -amplified breast cancers are associated with poor clinical outcome. These data suggest alterations in RR2MB and possibly the interacting 8q-proteins could have a profound effect on regulatory pathways such as DNA repair and cellular survival, highlighting therapeutic opportunities in these cancers.
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http://dx.doi.org/10.3389/fgene.2021.628758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045241PMC
March 2021

Healthcare Shift Workers' Temporal Habits for Eating, Sleeping, and Light Exposure: A Multi-Instrument Pilot Study.

J Circadian Rhythms 2020 Oct 21;18. Epub 2020 Oct 21.

School of Nursing, University at Buffalo, Buffalo, NY, US.

Background: Circadian misalignment can impair healthcare shift workers' physical and mental health, resulting in sleep deprivation, obesity, and chronic disease. This multidisciplinary research team assessed eating patterns and sleep/physical activity of healthcare workers on three different shifts (day, night, and rotating-shift). To date, no study of real-world shift workers' daily eating and sleep has utilized a largely-objective measurement.

Method: During this fourteen-day observational study, participants wore two devices (Actiwatch and Bite Technologies counter) to measure physical activity, sleep, light exposure, and eating time. Participants also reported food intake via food diaries on personal mobile devices.

Results: In fourteen (5 day-, 5 night-, and 4 rotating-shift) participants, no baseline difference in BMI was observed. Overall, rotating-shift workers consumed fewer calories and had less activity and sleep than day- and night-shift workers. For eating patterns, compared to night- and rotating-shift, day-shift workers ate more frequently during work days. Night workers, however, consumed more calories at work relative to day and rotating workers. For physical activity and sleep, night-shift workers had the highest activity and least sleep on work days.

Conclusion: This pilot study utilized primarily objective measurement to examine shift workers' habits outside the laboratory. Although no association between BMI and eating patterns/activity/sleep was observed across groups, a small, homogeneous sample may have influenced this. Overall, shift work was associated with 1) increased calorie intake and higher-fat and -carbohydrate diets and 2) sleep deprivation. A larger, more diverse sample can participate in future studies that objectively measure shift workers' real-world habits.
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http://dx.doi.org/10.5334/jcr.199DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583716PMC
October 2020

Amino Acid -mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights.

Biology (Basel) 2020 Oct 28;9(11). Epub 2020 Oct 28.

Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical and Computer Engineering, College of Engineering, Drexel University, Philadelphia, PA 19104, USA.

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide -mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid -mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide -mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.
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http://dx.doi.org/10.3390/biology9110365DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694136PMC
October 2020
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