Radiology departments are under increasing pressure to meet the demand for timely and accurate diagnostics, especially with chest x-rays, a key modality for pulmonary condition assessment. Producing comprehensive and accurate radiological reports is a time-consuming process prone to errors, particularly in high-volume clinical environments. Automated report generation plays a crucial role in alleviating radiologists' workload, improving diagnostic accuracy, and ensuring consistency. This paper introduces ChestX-Transcribe, a multimodal transformer model that combines the Swin Transformer for extracting high-resolution visual features with DistilGPT for generating clinically relevant, semantically rich medical reports. Trained on the Indiana University Chest x-ray dataset, ChestX-Transcribe demonstrates state-of-the-art performance across BLEU, ROUGE, and METEOR metrics, outperforming prior models in producing clinically meaningful reports. However, the reliance on the Indiana University dataset introduces potential limitations, including selection bias, as the dataset is collected from specific hospitals within the Indiana Network for Patient Care. This may result in underrepresentation of certain demographics or conditions not prevalent in those healthcare settings, potentially skewing model predictions when applied to more diverse populations or different clinical environments. Additionally, the ethical implications of handling sensitive medical data, including patient privacy and data security, are considered. Despite these challenges, ChestX-Transcribe shows promising potential for enhancing real-world radiology workflows by automating the creation of medical reports, reducing diagnostic errors, and improving efficiency. The findings highlight the transformative potential of multimodal transformers in healthcare, with future work focusing on improving model generalizability and optimizing clinical integration.

Spinal muscular atrophy (SMA) is an uncommon disorder associated with genes characterized by the gradual weakening and deterioration of muscles, often leading to substantial disability and premature mortality. Over the past decade, remarkable strides have been made in the field of SMA therapeutics, revolutionizing the landscape of patient care. One pivotal advancement is the development of gene-targeted therapies, such as nusinersen, onasemnogene abeparvovec and risdiplam which have demonstrated unprecedented efficacy in slowing disease progression. These therapies aim to address the root cause of SMA by targeting the survival motor neuron (SMN) gene, effectively restoring deficient SMN protein levels. The advent of these innovative approaches has transformed the prognosis for many SMA patients, offering a glimmer of hope where there was once limited therapeutic recourse. Furthermore, the emergence of small molecule compounds and RNA-targeting strategies has expanded the therapeutic arsenal against SMA. These novel interventions exhibit diverse mechanisms of action, including SMN protein stabilization and modulation of RNA splicing, showcasing the multifaceted nature of SMA treatment research. Collective efforts of pharmaceutical industries, research centers, and patient advocacy groups have played an important role in expediting the translation of scientific discoveries into visible clinical benefits. This review not only highlights the remarkable progress achieved in SMA therapeutics but also generates the ray of hope for the ongoing efforts required to enhance accessibility, optimize treatment strategies, rehabilitation (care and therapies) and ultimately pave the way for an improved quality of life for individuals affected by SMA.

Cancer is a deadly disease with complex pathophysiological nature and is the leading cause of death worldwide. Traditional diagnosis methods often detect c

This paper offers idea of orthogonal moments (OM) such as, Zernike Moments (ZM), and Pseudo Zernike Moments (PZM) for the analysis of melanoma images. Scale and phase invariance have been tested by varying the image sizes 32x32, 64x64, 128x128, and 256x256 and by varying phase angle from 10θ to 80θ degrees for the melanoma images. Magnitude of moment features have been extracted up to 32 orders for the cases m=0 and m=2. These extracted moment features are achieved accuracy is 91.24%, sensitivity is 86.78%, and specificity is 88.76% which is better than existing techniques. Optimized by Particle Swarm Optimization (PSO) and classified by Support Vector Machine (SVM).

(2020). Analysis of autopilot system, integrated with modelling and comparison of different controllers with the system. Journal of Discrete Mathematical Sciences and Cryptography. Ahead of Print.