Hemanth Noothalapati

Surface-enhanced Raman spectroscopy (SERS) achieves ultrasensitive molecular detection; however, reliable discrimination of structurally similar analytes remains a critical challenge due to spectral overlap. Here, we report a flexible aluminum (Al)-based SERS substrate that integrates efficient plasmonic hotspot generation with machine learning–assisted spectral analysis for the detection and differentiation of structurally similar organic dyes, including malachite green, crystal violet, and methylene blue. The as-prepared Al substrate underpins dense and reproducible hotspot formation, exhibiting a high enhancement factor (EF) of 2.11 × 1011 and enabling ultrasensitive detection down to a limit of detection (LOD) of 10–15 M. To address spectral complexity, a data-driven approach combining principal component analysis (PCA) and support vector machine (SVM) classification is employed, achieving a high classification accuracy of 96.7% for single analytes as well as binary and ternary mixtures across multiple concentration levels. The proposed approach offers strong potential for multiplex detection and practical applications in environmental monitoring, food safety, and on-site analytical sensing.

Chronic myeloid leukemia (CML) is a haematological malignancy that necessitates continuous monitoring for disease progression and resistance to tyrosine kinase inhibitors (TKIs). Current clinical assays are mechanism-specific, costly, and often inadequate for detecting resistance in the terminal phase, where underlying mechanisms remain poorly characterized. To address this limitation, we evaluated Raman spectroscopy (RS) as a mechanism-independent diagnostic approach capable of capturing comprehensive macromolecular profiles. Raman spectra were acquired from blood-derived cells, plasma, and serum samples of 32 CML patients and 12 healthy controls. Principal component analysis combined with linear discriminant analysis (PC-LDA) and multivariate curve resolution (MCR) revealed distinct biochemical differences between CML and control samples, including alterations in protein and fatty acid signatures. Classification accuracies achieved via PC-LDA were 70.73% (cells), 86.36% (plasma), and 88.64% (serum), with data fusion across sample types enhancing accuracy to 95.12–100%. Notably, MCR identified discriminative spectral components in plasma (component 6) and serum (component 5). These findings demonstrate the capacity of RS to differentiate CML from healthy states across multiple biological samples and identify serum as the most suitable material for future RS-based clinical diagnostics. This study provides a methodological foundation for future work toward developing mechanism-independent RS platforms for clinical monitoring in CML.

Globally, the livestock sector plays a crucial role in ensuring food security and nutrition while supporting livelihoods of millions. However, antimicrobial resistance (AMR) poses a serious threat to the availability, accessibility, and safety of livestock-derived products. AMR has significant impacts on society in the form of increased mortality, rising healthcare costs, loss in Gross Domestic Product, and driving large populations into poverty by Rendering Many treatment options ineffective. The impact of AMR is projected to rise globally, with over ten million related deaths predicted annually by 2050—equivalent to the current global annual death toll from cancer. The challenge with AMR is its rapid evolution, outpacing our capacity to manage it. Livestock farming systems are a major source of AMR transmission, driven by the rising global demand for protein alongside stagnation in cereal production. In this review, we provide a concise overview of the development of AMR in livestock, the modes of transmission, mechanisms of antimicrobial resistance gene transfer, along with One Health approaches and other control strategies. Furthermore, we highlight the use of artificial intelligence algorithms to process extensive data from diverse sources, utilize historical data to forecast potential AMR outbreaks, swiftly examine bacterial genomes to detect AMR genes, and predict dissemination, providing a basis for choosing the optimal antibiotics for individual patients based on unique infection and resistance profiles.