Sharda University

Accurate prediction of the Klinkenberg slippage factor (KSF) is crucial for characterizing low permeability gas reservoirs, but conventional laboratory determination methods are prohibitively costly and time-consuming, constituting a major research gap. This study addresses this limitation by presenting a novel, experimentally validated workflow that seamlessly integrates comprehensive laboratory measurements with advanced machine learning (ML) models to achieve rapid and accurate KSF prediction. A robust dataset of 253 limestone core samples was utilized, employing air permeability, cation exchange capacity (Qv), porosity, tortuosity and grain density as input features. After a systematic comparative evaluation of ten algorithms on the capacity of ML to model the complex, non-linear relationships in KSF estimation are demonstrated. The key distinction and novelty reside in the superior performance of the AdaBoost algorithm, which achieved the highest predictive accuracy (R2=0.998, AARE%=1.276). This research offers a robust, efficient, and cost-effective alternative to traditional permeability correction techniques, establishing an innovative, data-driven framework that significantly enhances predictive modeling for reservoir evaluation in tight gas formations.

Alzheimer's disease (AD) is a multidimensional neurodegenerative disease leading to progressive loss of cognitive function and a growing health burden on the world population. Although decades of research have been conducted on this disease, current therapies have limited clinical value, mainly because researchers have not fully incorporated the intricate molecular pathways underlying its development and progression. This review summarizes current knowledge of AD pathophysiology, including amyloid beta (Aβ) dysregulation, tau hyperphosphorylation, neuroinflammation, mitochondrial dysfunction, oxidative stress, and synaptic breakdown. Although the amyloid- and tau-centered paradigms remain prevailing in the field, we note newer molecular targets, including secretase modulators, inflammatory signaling hubs, mitotic and autophagic regulators, epigenetics, and synaptogenesis pathways. We prioritize mechanistic, structural, cellular, and systems levels to facilitate a rational development of therapeutic understanding. The latest trends in medicinal chemistry and computational drug design, multi-target- directed ligands and hybrid scaffolds, as well as in silico ADMET optimization, are also discussed. Furthermore, we discuss the therapeutic aspects of bioinspired analogues of natural products. Lastly, we discuss the ongoing clinical development initiatives, opportunities, and major translational issues. In general, we highlight the need for integrative, mechanism-oriented, and personalized treatment approaches to propel the next generation of AD therapies.

Tuberculosis (TB) accounted for almost 1.2 million deaths in 2024. It remains a leading cause of mortality due to an airborne infectious bacterium, Mycobacterium tuberculosis (MTB). Despite the development of vaccines and antibiotics to cure the disease, it remains a major global problem due to the development of several ingenious pathogenic pathways. This comprehensive review introduces the mechanisms of bacterial pathogenesis against the human defence system and the development of drugs against MTB, with particular emphasis on the mechanisms of drug resistance and mutations that render them ineffective globally. There is also a discussion of the MTB lineage and the mutant types. The mechanisms of multidrug resistance and ineffective treatment regimens have driven advances in drug development and alternative therapeutics. New molecules that can potentially disarm MTB have been explored, including SQ109, GuaB2, Q203, Largazole, and Auranofin. In addition, natural compounds, bacteriophage therapy, antimicrobial peptides, and probiotics are also explored to help address the global threat posed by MTB.

The gut microbiome is fundamental to gastrointestinal and systemic homeostasis through its interactions with dietary components and host-derived factors. Substantial evidence demonstrates a close functional association between the gut and the central nervous system (CNS), establishing a complex communication network that is essential for both health and disease. This review examines the role of the gut microbiota in regulating gastrointestinal physiology and brain function, with particular emphasis on the microbiota-gut-brain axis and its bidirectional signaling mechanisms. The gut microbiota supports normal brain function and gastrointestinal physiology through complex molecular interactions involving the enteric nervous system, neuromuscular junctions, and the CNS. Disruption of microbial balance, known as gut dysbiosis, results in impaired gut integrity, increased intestinal permeability, compromised blood-brain barrier function, and altered neuroimmune signaling. These changes are closely linked to the development and progression of neurological disorders. The microbiota-gut-brain axis constitutes a critical regulatory system that connects gastrointestinal and neurological health. Advancing the understanding of microbiota-mediated mechanisms may yield novel insights into disease pathogenesis and facilitate the development of microbiome-targeted therapeutic strategies.

Microsphere-based technologies have increasingly gained attention as adaptable and multifunctional tools in breast cancer research and clinical care. Traditional approaches, including two‑dimensional cell cultures, systemic drug delivery, and broad diagnostic methods, often fail to accurately mimic tumor behavior or deliver therapeutics efficiently. Microspheres, with their customizable size, composition, mechanical characteristics, and surface chemistry, offer a platform that can overcome many of these limitations. Their ability to modulate cell–material interactions and control local drug release positions them as valuable components in modern cancer modeling, diagnosis, and therapy. The primary aim of this review is to synthesize current advancements in microsphere technologies as they apply to breast cancer. The review seeks to evaluate how microsphere-based systems contribute to improved tumor modeling, enhanced diagnostic accuracy, and more effective therapeutic strategies. Additionally, it identifies existing challenges and defines future directions needed to translate these technologies into routine clinical use. This review explores microsphere technologies in breast cancer research and clinical applications. It highlights their roles in three‑dimensional tumor modeling, advanced diagnostic platforms integrating imaging, electrochemical sensors, and microfluidics, and controlled drug delivery systems for chemotherapeutic and endocrine therapies. The review also discusses stimuli‑responsive microspheres enabling targeted release and clinical uses such as transarterial chemoembolization and yttrium‑90 radioembolization. Evidence from experimental, preclinical, and clinical studies is synthesized to evaluate current progress and future potential. Findings indicate that microsphere-enabled three‑dimensional tumor culture systems better replicate key hallmarks of breast cancer biology compared to traditional two‑dimensional platforms. These 3D systems capture tumor architecture, mechanobiology, metabolic diversity, and drug resistance behavior more accurately, improving the predictive value of drug screening. Diagnostic innovations demonstrate that functionalized microspheres significantly enhance analytical sensitivity, allow dynamic monitoring of tumor biomarkers, and improve cell tracking capabilities across imaging and sensor-based platforms. Therapeutic applications show that microspheres provide sustained and localized drug delivery, reducing systemic toxicity and enhancing treatment efficacy. Stimuli-responsive microspheres allow precisely targeted and temporally coordinated release, enabling more personalized and adaptive treatment strategies. Microsphere-based technologies represent a powerful and multifaceted toolkit for advancing breast cancer research, diagnosis, and therapy. Their versatility enables improved disease modeling, more sensitive and real-time diagnostics, and effective localized treatment strategies. Despite substantial progress, persistent challenges, such as standardization, biological complexity, reproducibility, scalability, and integration into routine clinical workflows, continue to hinder widespread adoption.