JIS University

Osteoporosis is a systemic skeletal disease characterized by impairment of bone remodeling, reduced bone strength, and fragility fractures. Despite active exploration of hydrogel-enabled 3D printing and bioprinting as tools for engineering bone tissue, the therapeutic implications for osteoporosis are still debated since most of the available data derive from general bone defect and osteochondral repair models. The purpose of this review is to discuss the role of bone remodeling in osteoporosis and analyse recent advances in hydrogel-based 3D-printed bioinks for bone regeneration. Particular attention is paid to their material composition and printability, mechanical properties, crosslinking strategies, biological performance, and ability to deliver a therapeutically relevant payload. The reviewed studies are classified according to the applied model and divided into general bone regeneration, osteoporosis-related bone-defect repair, localized therapy, and systemically active treatment. The limitations in terms of mechanical properties, vascularization, biodegradation, biosafety, and the lack of relevant in vivo preclinical models are discussed. This review aims to highlight the opportunities of hydrogel-based 3D-printed constructs for the regeneration and local or systemic treatment of osteoporotic bone while emphasizing the need for disease-specific and clinically relevant approaches.

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.

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.

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.

Surge-like glacier instabilities in Central Asia remain underexplored, particularly in regions of mild instability or smaller glaciers. In 1980, two leading Soviet glaciologists proposed a classification method (GS1980) to calculate the spatial distribution of “pulsating” glaciers in the Hissar–Alay range, predicting a 20% prevalence of unstable flow and claiming highly accurate detection. These findings were unconfirmed in subsequent studies, which typically reported fewer than 10 surge-type glaciers in the region. Here, we address this discrepancy by reassessing the GS1980 predictions using a newly compiled multi-sensor satellite dataset covering nearly six decades. We systematically examine glacier dynamics in the region, assessing ice flow instabilities from changes in terminus position, ice thickness, and surface morphology. We identify 171 glaciers that exhibit pulsating behavior, corresponding to 25% of the sample—in broad agreement with GS1980. Flow instabilities tend to be modest in scale, with slow advances and long active phases (mean duration of 14 years). We find that the GS1980 model shows some ability to distinguish pulsating from stable-flowing glaciers; however, its predictive power is lower than claimed due to the simplifying assumptions of its morphology-based approach and the uncertainties in the input data. Our results indicate that pulsations in the region are more widespread than previously reported, but fall at the weaker end of the spectrum of glacier instability, which may not be well represented by a sharp binary classification (surge-type versus stable). As more detailed satellite records become available, we suggest that a more nuanced framework may be useful to recognize and interpret subtler instabilities of small glaciers.