Jaipur National University

Cancer continues to pose a global health challenge, with conventional therapies often limited by non-specific toxicity, drug resistance, and an inadequate therapeutic index. Nanotechnology offers transformative opportunities by enabling targeted drug delivery, improved pharmacokinetics, and integrated diagnostic-therapeutic platforms (termed nanotheranostics). This review highlights key nanocarrier systems including liposomes, polymeric nanoparticles, dendrimers, inorganic nanostructures, carbon-based materials, extracellular vesicles, and hybrid platforms with a focus on human studies and clinical translation. Design strategies (such as passive and active tumor targeting, biomimicry, and stimuli-responsive release mechanisms) are discussed in the context of improving tumor selectivity and minimizing systemic toxicity. Recent innovations, including AI-supported nanomedicine design, smart nanorobots, and cell-mediated delivery systems, are also examined. Although multiple nano-formulations such as Doxil®, Abraxane®, and Vyxeos® have reached clinical use, challenges remain including large-scale manufacturing, regulatory pathways, long-term safety evaluation, and cost-effective global accessibility. This review provides a critical appraisal of current evidence, translational bottlenecks, and emerging opportunities to guide future nanomedicine development. Nanotechnology is poised to become a cornerstone of precision oncology, enabling personalized, safe, and effective cancer treatment paradigms.

Chimeric Antigen Receptor T-cell (CAR-T cell) therapy is a promising cancer treatment that has shown success in treating certain type of cancer. It has demonstrated remarkable efficacy, particularly in hematologic malignancies such as certain types of leukemia and lymphomas, offering hope for patients with limited options. In this review the authors highlighted the importance of CAR-T cell therapy in oncology, history, recent innovations in CAR-T cell engineering and development, mechanism of action, toxic effects, clinical trials and ongoing research on CAR-T cell therapy. Barriers to effective CAR-T cell therapy includes antigen escape, tumor heterogeneity, microenvironment of the tumor, On target, Off tumor toxicity, modest anti-tumor activity and limited tumor infiltration. In this review we discussed global regulations of CAR-T cell therapies, challenges of harmonized regulations, FDA approved CAR-T cell therapies, clinical applications, clinical trials and research and future prospects of CAR-T cell therapy.

The search for lead-free, stable, and high-performance perovskite materials for photovoltaic applications has motivated extensive research into novel compositions. Here, we present a comprehensive first-principles study of gold-based halide perovskites AuGeX3 (X = Cl, Br, I) using density functional theory (DFT) and time-dependent DFT (TD-DFT) with the APFD/LanL08 approach. Structural analysis confirms the thermodynamic stability and experimental viability of these materials in the cubic perovskite phase, with Goldschmidt tolerance factors (0.92-0.96) and negative formation energies supporting their feasibility. A key finding is the systematic tunability of electronic properties through halide substitution: the HOMO-LUMO energy gap decreases progressively from 1.60 eV (Cl) to 1.18 eV (I), positioning AuGeI3 within the optimal range (1.1-1.4 eV) defined by the Shockley-Queisser (S-Q) limit for single-junction solar cells. Based on the S-Q detailed balance model, the theoretical maximum efficiency for an ideal AuGeI3 absorber is approximately 32.2%, representing a fundamental upper bound under perfect conditions, with actual device performance expected to be lower due to practical loss mechanisms. Quantum chemical descriptors reveal that the iodide variant exhibits enhanced chemical softness and charge transfer capacity, favorable for photovoltaic operation. Optically, AuGeI3 demonstrates outstanding properties, including a high dielectric constant (epsilon up to 11.2) and refractive index (n up to 4.4), promoting effective charge screening and strong light-matter interaction. TD-DFT simulations further confirm robust absorption across the visible to near-infrared regions with high oscillator strength (f = 0.10). Thermal analysis indicates sufficient stability and structural adaptability for device applications. Collectively, these combined attributes, optimal band gap, superior optical response, and thermal resilience, establish AuGeX3 perovskites, particularly AuGeI3, as highly promising lead-free candidates for next-generation optoelectronics. We emphasize that experimental validation through synthesis and characterization is now essential to confirm these computational predictions and translate them into practical devices.

Mitochondrial dysfunction has emerged as a central contributor to the pathogenesis of major neurodegenerative disorders, such as Parkinson's and Huntington's disease. In Parkinson's disease, mitochondrial abnormalities are often linked to mutations in genes like PINK1 and Parkin, which regulate mitochondrial quality control, while α-synuclein aggregation further exacerbates mitochondrial damage. In Huntington's disease, mutant huntingtin protein impairs mitochondrial dynamics, transport, and ATP production, contributing to selective neuronal vulnerability. The convergence of mitochondrial impairments across both diseases highlights a common pathological axis that can be therapeutically targeted. This review critically examines the molecular underpinnings of mitochondrial dysfunction in PD and HD and explores emerging strategies to restore mitochondrial function. These include antioxidants, metabolic modulators, mitophagy activators, and gene therapy approaches. Despite promising preclinical findings, several translational challenges remain, underscoring the need for continued investigation. Understanding the shared and unique mitochondrial-related mechanisms in PD and HD will be essential for developing targeted, disease-modifying therapies that may improve outcomes and quality of life for affected individuals.

Cloud infrastructure development has created new security problems which demand real-time detection systems to fight complex cyber threats. The given manuscript introduces an Adaptive Cloud Security Risk Assessment Framework (ACSRF) which uses Pattern-Aware Countermeasure Optimization through a four-layered system namely (i) Attack Pattern Identification Layer that uses deep learning and graph-based anomaly detection to predict advanced attack methods, (ii) Countermeasure Generation Layer that generates real-time defense strategies through reinforcement learning (RL) with adversarial simulation (iii) Ontology Design Layer that selects optimal countermeasures by merging trust-weighted policy evaluation with multi-criteria decision-making (MCDCM) and (iv) System Optimization and Validation Layer that implements federated learning and Bayesian optimization (BO) for real-time policy optimization based on performance requirements. The proposed framework demonstrates superior performance than traditional rule-based and static defense systems through its ability to detect attacks with 3.7 times better accuracy and respond 2.8 times faster while delivering enhanced adaptive countermeasure effectiveness without affecting cloud system performance.