JIS University

Breast cancer is one of the major health concern and the second leading cause of death among women globally. The survival rates in breast cancer depends on the stages (Stage I–Stage IV), there by the early diagnosis and followed by surgery and chemotherapy is highly recommended. Conventional treatments, such as chemotherapy, surgery often have limited efficacy and are associated with severe side effects in breast cancers. Thereby, biosafe materials with high potency is in high demand. Phytochemical loaded nano materials are bio compatible, bio safe as a result, that can be explored in breast cancer therapy with least toxicity effect to other healthy tissues. Exploring the potentiality of targeted drug delivery approaches to mitigate breast cancer, focusing on plant-based bioactive molecules (phytochemicals) and their coupling with nano carriers to overcome the different limitations of traditional therapies. The utilization of phytochemicals in breast cancer management, known for their safety and therapeutic efficacy, is discussed as an alternative approach in this review. Challenges such as poor bioavailability, short half-life, and lack of site specificity, which limit their clinical application, are addressed in different sections. Strategies for mitigating these drawbacks include conjugating phytochemicals with nanocarriers such as liposomes, polymeric nanoparticles, metallic nanoparticles, and carbon dots have also been described in this review. Nanocarriers enhance the stability, systemic bioavailability, and site-specific delivery of phytochemicals, enabling them to cross biological barriers effectively while reducing normal cell toxicity. These systems provide a “green corridor” to target breast cancer cells with improved therapeutic efficacy. Ongoing research and clinical trials highlight the promise of phytochemicals conjugated with nanocarriers in breast cancer therapy. This innovative therapeutic approach has the potential to revolutionize breast cancer management. Further research should focus on advancing the development and clinical application of phytochemicals conjugated with nanocarriers to ensure their widespread adoption in breast cancer therapy.

The control of insecurity, transparency, and real-time tracking of assets are the urgent challenges of the construction supply chain because of the complex multi-stakeholder structure and the emergence of quantum computing threats. This paper applies a four-phase systematic approach to developing a Blockchain-based Construction Supply Chain (BCSC) framework, which includes: (1) architectural design of Hyperledger Fabric private blockchain with IoT-enabled real-time tracking, (2) smart contracts to process automated procurement, inventory, and payment, (3) quantum-resilient cryptography based on lattice-based encryption, approachable post-quantum secure digital signatures and (4) lightweight Federated Learning models to predictive analytics. The framework was tested with comparative performance analysis of the conventional blockchain solutions and confirmed by the real-world application of the same at SIL, a multinational construction company. The experiment outcomes prove significant improvements: security resiliency is 50% better, transactions latency decreases by 40% and traceability efficiency increases by 35% as compared to traditional blockchain-based supply chain solutions.

The centralized architectures of global vaccine supply chains pose severe security, latency, and operational efficiency challenges to new threats of quantum computing. The article introduces the initial combined edge-enabled quantum-safe real-time optimization of vaccine supply chain based on multi-access edge computing (MEC), post-quantum cryptography, lightweight machine learning, and blockchain consensus algorithms. The framework uses autonomous decision-making distributed edge nodes, and uses novel algorithms such as hierarchical attention network to resource allocation (HAN-RA), quantum-inspired route optimization (QIRO) and adaptive multi-agent reinforcement learning (AMARL). CRYSTALS-Kyber, CRYSTALS-Dilithium and SPHINCS+ are post-quantum cryptographic protocols that are resistant to both classical and quantum adversaries. The improvements in performance have been verified experimentally: 68.2 percentage latency reduction (245 to 78 ms), 188.2 percentage throughput increase (850 to 2,450 throughput (TPS)), 19.5 percentage increase in security score (82 to 98/100), and any number of nodes can be scaled linearly to 10,000 nodes. The system has a 97.2% temperature breach detection, 94.8% high-demand prediction and 99.97% uptime during nonstop 72-h operations. This quantum-resistant architecture operates to fill key gaps in existing supply chain models, offering a scalable, secure and efficient system to support mission-critical healthcare logistics and developing theoretical bases of next-generation post-quantum distributed computing systems.

Cyclic dinucleotides (CDNs), such as cyclic di-AMP (c-di-AMP) and cyclic di-GMP (c-di-GMP), are key second messengers that regulate fundamental bacterial processes, including cell wall synthesis, biofilm formation, antibiotic resistance, stress response, and virulence. These pathways are particularly relevant in major pathogens like Mycobacterium tuberculosis. Increasing evidence highlights the pathogenic potential of non-tuberculous mycobacteria (NTM), originally environmental species that are emerging as significant human pathogens. Understanding the evolution of CDN signaling may therefore provide critical insights into this transition. Our comparative genomic analysis revealed that the c-di-AMP synthase disA is present as a single copy in nearly all mycobacterial genomes, except within the genus Mycolicibacter.The corresponding phosphodiesterases, pde and ataC, are variably distributed, with pathogenic mycobacteria showing a preference for pde over ataC. In contrast to the relatively conserved c-di-AMP system, the c-di-GMP pathway comprising diguanylate cyclases (DGCs) and phosphodiesterases (PDEs), exhibits remarkable variation in gene presence/absence and domain architecture across the Mycobacteriaceae family. This diversity suggests multiple independent gene gain and loss events throughout evolution, often accompanied by the acquisition of accessory domains. Evolutionary analyses reveal a clear dichotomy between the two CDN signaling systems. The c-di-AMP pathway, governed by disA, which is under strong purifying selection similar to core housekeeping genes, and pde, which shows low genetic variability, underscores its conserved and essential role in maintaining core cellular physiology. In contrast, the c-di-GMP system is markedly more heterogeneous, consistent with its function in environmental sensing and adaptation. Together, these findings highlight a sharp evolutionary split in CDN signaling within the Mycobacteriaceae family, c-di-AMP serves as an indispensable regulator of core physiological processes, whereas c-di-GMP confers flexibility for niche-specific adaptation and survival.

Analyzing medical images is an essential aspect of computer-aided diagnosis in the modern era. It facilitates timely detection and accurate identification of diseases. Nevertheless, traditional deep learning approaches encounter difficulties in consistently performing across different imaging modalities because of challenges including: high intra-class variability, image noise, and a lack of labeled data for supervised learning. This work proposes a Hybrid CNN-LSTM-Attention architecture to address these challenges, blending i) the spatio-temporal learning capability of CNNs, ii) the temporal modeling ability of LSTM networks, and iii) the contextual focusing capability of an Attention module. The CNN used in the proposed hybrid architecture learns deep hierarchical spatio-temporal features, the LSTM networks learn sequential dependencies, and the Attention module learn to focus on areas significant for diagnosis, leading to improvements in both accuracy, and interpretability of the model. The system evaluated the hybrid CNN-LSTM-Attention framework across several medical imaging benchmarks, including Chest X-Ray (Pneumonia vs. Normal), MRI Brain Tumor Classification and Retinal OCT. The proposed architecture achieved 95.62 % accuracy, 94.78 % precision, 95.03 % recall, and $94.90 \% ~\mathrm{F} 1$-score, which outperformed baseline CNN and CNN-LSTM baseline models, as well as transformer architectures.