The COVID-19 pandemic irrevocably altered our perception of public health, hygiene, and personal interaction, accelerating the demand for solutions that minimize physical contact. Once, using a fingerprint scanner, pressing a PIN, or touching a shared surface felt routine. During the pandemic, these simple actions suddenly became potential vectors for contagion. Touchless biometrics — technologies that capture unique biological characteristics without physical contact — have emerged as a crucial response. From facial recognition at airports to voice authentication in banking apps, “no touch required” is becoming the new standard, combining hygiene, convenience, and security. More >

Diabetic retinopathy (DR) is a leading cause of blindness globally, requiring timely detection and classification to prevent vision loss. Deep learning techniques offer significant potential for automating DR detection by analyzing retinal fundus images with high precision. This paper proposes a RetinoNet model that consists of MobileNetV3, Convolutional Block Attention Module (CBAM), Atrous Spatial Pyramid Pooling (ASPP), and Feature Pyramid Network (FPN). MobileNetV3 provides a lightweight and efficient foundation for feature extraction, while CBAM emphasizes critical spatial and channel information, enabling the detection of subtle retinal abnormalities. ASPP captures multi-scale contextu... More >

Compared with traditional storage media, biological data storage has advanced more rapidly in capacity, diversity, and lifespan, and it also enables continuous data retention. The DNA molecule, as Nature's own archival medium, provides unparalleled density, longevity, and passive durability, making it a compelling foundation for the next generation of "cold" and "deeply cold" archives. Since 2019, progress across the stack—coding for insertion–deletion–missing channels, large-scale random access, enzyme writing, nanopore-native retrieval, and chemically robust expansion—has transformed DNA storage from provocative demonstrations into mature technologies with early end-to-end prototyp... More >

DNA data storage is a promising technology that utilizes computer simulation and offers high-density and durable digital information storage. It is challenging to store massive image data in a small amount of DNA without losing the original data since nonspecific hybridization errors occur frequently and severely affect the durability of stored data. This work proposes a novel approach (RFS-codec) comprising an image fraction strategy and an innovative codec method to split and encode image data into DNA storage, respectively. The fraction strategy contributes by delivering a cost-effective solution for image storage in DNA. The codec method offers an encryption mechanism to convert binary d... More >

Artificial intelligence (AI) is transforming personalized medicine through its potential. However, implementing AI-driven healthcare solutions remains inconsistent because of certain social factors, i.e., cultural beliefs, trust issues, and major accessibility elements. This study focuses on the key social determinants involved in the acceptance and implementation of AI-driven medicine, with a prime focus on hurdles such as algorithmic bias, transparency issues, and public skepticism. A quantitative approach was employed in the research, and survey data were collected among healthcare professionals, policymakers, and patients. Statistical analyses were performed, including chi-square tests a... More >

As a carrier of genetic information, the precise design and optimization of nucleic acid (DNA or RNA) sequences are of critical importance for the realization of specific biological functions. From synthesizing genes to designing novel nucleic acid drugs, from constructing efficient expression vectors to modifying microbial metabolic pathways, all are inseparable from the fine regulation of nucleic acid sequences. The primary purpose of nucleic acid sequence design is to generate new sequences tailored to specific requirements for gene expression, function prediction, drug development, and other applications. In this paper, we first review the theoretical basis of nucleic acid sequence desig... More >

Social media has significantly transformed the digital landscape by enabling an unprecedented expansion of content, further accelerated by the COVID-19 pandemic, which increased the demand for online classes, virtual meetings, and recorded conferences. While major technology companies have previously employed sentiment analysis and opinion mining to gauge user feedback, this study proposes a novel framework for emotion-based video content analysis. The proposed method extracts audio from social media videos and applies Speech-to-Text (STT) conversion. The extracted text is then processed using a pre-trained BERT model, leveraging its fine-tuned capabilities and 110 million parameters to enha... More >

Presents the inaugural editorial for this issue of the publication. More >