Biology subjects

Akanksha,

Publications and source records attributed to Akanksha,.

2 recordsLinked to original sources

bioRxiv2023

Temporally variable drug profiles select for diverse adaptive pathways despite conservation of efflux-based resistance mechanism

Antibiotic resistance is a global health concern with emergence of resistance in bacteria out-competing the discovery of novel drug candidates. While Adaptive Laboratory Evolution (ALE) has been used to identify bacterial resistance determinants, most studies investigate evolution under stepwise increasing drug profiles. Thus, bacterial adaptation under long-term constant drug concentration, a physiologically relevant profile, remains underestimated. Using ALE of Mycobacterium smegmatis subjected to a range of Norfloxacin concentrations under both constant and stepwise increasing drug dosage, we investigated the impact of variation of drug profiles on resistance evolution. All the evolved mutants exhibited a drug concentration dependent increase in resistance accompanied with an increase in the number of mutations. Mutations in an efflux pump regulator, LfrR, were found in all the evolved populations suggesting conservation of an efflux-based resistance mechanism. The selection of these mutations was tightly coupled to the presence of its regulated gene in the genetic background. Further, lfrR mutations appeared early during the adaptive trajectory and imparted low-level resistance. Subsequently, sequential acquisition of other mutations, dependent on the drug profile, led to high-level resistance emergence. While divergent mutational trajectories led to comparable phenotype, populations evolved under constant drug exposure accumulated mutations in dehydrogenase genes whereas in populations under increasing drug exposure, mutations in additional regulatory genes were selected. Our data also shows that irrespective of the evolutionary trajectory, drug target mutations were not selected up to 4X drug concentration. Overall, this work demonstrates that evolutionary trajectory is strongly influenced by the drug profile.

evolutionary biology↗

bioRxiv2021

Multi-taskLearning and Ensemble Approach to Predict Cognitive Scores for Patients with Alzheimer's Disease

During its chronic degenerative course, Alzheimers Disease severely harms the patients cognitive abilities. Assessment of current and future cognition is an integral component of a diagnosis of dementia, and therefore an important clinical and scientific goal. Unfortunately, subjective, time-consuming and operator-sensitive clinical surveys or neuropyschiatric batteries remain the only viable methods of assessing cognition. Given that MRI is the most prevalent, cost-effective, and clinically important imaging modality, it may be considered a suitable predictor of cognition. Yet, it has hitherto proved very challenging to predict one from the other. We propose that an image-based Deep Learning model can be custom-built to achieve this goal. We designed a novel multi-task UNet model to predict the subjects current and future cognition (via ADAS-Cog scores), taking as input baseline T1-weighted MRI and demographic risk factors. The key innovation in the model is that it seeks to solve two adjacent but relevant tasks: image segmentation into tissue types; and prediction of cognition. The first task gives a high-accuracy brain segmentation, comparable to other cutting edge methods. The features trained from the segmentation task are used in the cognition task. This combination is far superior to stand-alone single-shot cognition models. We achieved excellent accuracy in both baseline and time-series forecast of ADAS-Cog scores. Through further feature map analysis made on the receptive fields, we managed to impart much-needed model interpretability, critical for real-world clinical practice. This study constitutes the best-reported performance of any comparable approach, and opens the door towards machine-based tracking of AD progression.

neuroscience↗