Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae family and Henipavirus genus is transmitted by Pteropus bats. Climate-induced changes in bat migration patterns and food availability enhances the virus’s adaptability, in turn increasing the potential for transmission and outbreak risk. NiV infection has high human fatality rate. With no antiviral drugs or vaccines available, exploring the complex machinery involved in viral RNA synthesis presents a promising target for therapy. Drug repurposing provides a fast-track approach by identifying existing drugs with potential to target NiV RNA-dependent RNA polymerase (L), bypassing the time-consuming process of developing novel compounds. To facilitate this, we developed an attention-based deep learning model that utilizes pharmacophore properties of the active sites and their binding efficacy with NiV L protein. Around 500 FDA-approved drugs were filtered and assessed for their ability to bind NiV L protein. Compared to the control Remdesivir, we identified Cangrelor, an antiplatelet drug for cardiovascular diseases, with stronger binding affinity to NiV L (glide score of -12.30 kcal/mol). Molecular dynamics simulations further revealed stable binding (RMSD of 3.54 Å) and a post-MD binding energy of -181.84 kcal/mol. The strong binding of Cangrelor is illustrated through trajectory analysis, principal component analysis, and solvent accessible surface area, further confirming the stable interaction with the active site of NiV RdRp. Cangrelor can interact with NiV L protein and may potentially interfere with its replication. These findings suggest that Cangrelor will be a potential drug candidate that can effectively interact with the NiV L protein and potentially disrupt the viral replication. Further in vivo studies are warranted to explore its potential as a repurposable antiviral drug.

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions.

Classical number theory has developed many descriptors for integers, including predicates, arithmetic functions, and structural invariants. The majority of them capture a specific aspect of an integer rather than providing a unified representation across multiple mathematical domains. We introduce the Universal Entropy Fingerprint Framework (UEFT), a computational framework for representing integers through multiple Shannon entropy coordinates derived from selected mathematical transformations across five complementary domains: algebraic (prime-exponent structure), geometric (divisor-lattice rank distribution), arithmetic (σ-weighted divisor magnitude), positional (multi-base digit distribution), and dynamical (Collatz trajectory parity). Every coordinate is computed using a single Shannon entropy definition, avoiding the ad hoc mixing of incompatible information measures that has limited previous integer-invariant proposals. We compute the full fingerprint for all integers up to N = 1,000,000 using sieve-based factorization and closed-form combinatorial identities, eliminating the need to explicitly construct divisor graphs or partition objects. The resulting 19-coordinate fingerprint space requires 17 of 19 standardized coordinates to reach 95% cumulative variance under Principal Component Analysis (PCA); this is partly driven by the 15 near-collinear positional coordinates. Exemplar integers spanning primes, prime powers, highly composite, and perfect numbers exhibit distinct fingerprint profiles, illustrating how classical arithmetic categories are represented within a continuous information-geometric space.