Biology subjects

Purohit, K.

Publications and source records attributed to Purohit, K..

3 recordsLinked to original sources

bioRxiv2024

Development of a pipeline to evaluate YAP-TEAD inhibitor potential - direct TEAD inhibition represses cancerous growth in a NF2-mutant model of mesothelioma

As the core, tumorigenic downstream effectors of the Hippo signalling pathway, YAP/TAZ and the TEAD family of transcription factors represent attractive targets for drug discovery efforts within cancer research. This is particularly true within the context of pleural mesothelioma, in which there are many recent preclinical developments and clinical trials evaluating the efficacy of TEAD inhibitors. The range of inhibitors have shown great promise, but comparisons of their performances are so far limited. Here we develop a high content pipeline that enables a comparative analysis of currently developed YAP/TAZ-TEAD inhibitors. We take advantage of isogenic cellular models that enable us to examine inhibitor specificity. We identify genetic compensation of the Hippo pathway transcriptional module, with implications for therapeutic targeting, and implement Cell Painting to develop a detailed morphological profiling pipeline that enables further characterisation, quantification, and analysis of off-target effects. Our pipeline is scalable and allows us to establish specificity and comparative potency within cancer relevant assays in a clinically relevant cellular model.

cancer biology↗

bioRxiv2024

Performance of GPT-4 on the American College of Radiology In-Service Examination

ObjectivesNo study has evaluated the ability of ChatGPT-4 to answer image-rich diagnostic radiology board exam questions or assessed for model drift in GPT-4s image interpretation abilities. In our study we evaluate GPT-4s performance on the American College of Radiology (ACR) 2022 Diagnostic Radiology In-Training Examination (DXIT). MethodsQuestions were sequentially input into GPT-4 with a standardized prompt. Each answer was recorded and overall accuracy was calculated, as was logic-adjusted accuracy, and accuracy on image-based questions. This experiment was repeated several months later to assess for model drift. ResultsGPT-4 achieved 58.5% overall accuracy, lower than the PGY-3 average (61.9%) but higher than the PGY-2 average (52.8%). Adjusted accuracy was 52.8%. GPT-4 showed significantly higher (p = 0.012) confidence for correct answers (87.1%) compared to incorrect (84.0%). Performance on image-based questions was notably poorer (p < 0.001) at 45.4% compared to text-only questions (80.0%), with adjusted accuracy for image questions of 36.4%. When the questions were repeated, GPT-4 chose a different answer 25.5% of the time and there was a small but insignificant decrease in accuracy. DiscussionGPT-4 performed between PGY-2 and PGY-3 levels on the 2022 DXIT, but significantly poorer on image-based questions, and with large variability in answer choices across time points. This study underscores the potential and risks of using minimally-prompted general AI models in interpreting radiologic images as a diagnostic tool. Implementers of general AI radiology systems should exercise caution given the possibility of spurious yet confident responses.

bioinformatics↗

bioRxiv2020

COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

COVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

bioengineering↗