Cellular senescence regulates aging, tissue maintenance, and disease progression through the Senescence-Associated Secretory Phenotype (SASP), a secretory profile of cytokines, chemokines, growth factors, and matrix-remodeling enzymes. While transient SASP aids wound healing, its chronic activation drives inflammation, fibrosis, and tumorigenesis. This review examines SASP’s molecular regulation, dual roles in health and pathology, and therapeutic potential. The following two main strategies are explored: senescence clearance, which eliminates SASP-producing cells, and SASP modulation, which refines secretion to suppress inflammation while maintaining regenerative effects. Key pathways, including NF-κB, C/EBPβ, and cGAS-STING, are discussed alongside pharmacological, immunotherapeutic, gene-editing, and epigenetic interventions. SASP heterogeneity necessitates tissue-specific biomarkers for personalized therapies. Challenges include immune interactions, long-term safety, and ethical considerations. SASP modulation emerges as a promising strategy for aging, oncology, and tissue repair, with future advancements relying on multi-omics and AI-driven insights to optimize clinical outcomes.

Community-acquired infections caused by Klebsiella pneumoniae (K. pneumoniae) have become a significant global health concern, particularly with the emergence of hypervirulent strains (hvKP). These strains are associated with severe infections, such as pyogenic liver abscesses, even in otherwise healthy individuals. Initially reported in Taiwan in the 1980s, hvKP has now spread worldwide. The pathogenicity of hvKP is attributed to an array of virulence factors that enhance its ability to colonize and evade host immune defenses. Additionally, the convergence of hypervirulence with antibiotic resistance has further complicated treatment strategies. As a member of the ESKAPE group of pathogens, K. pneumoniae exhibits high resistance to multiple antibiotics, posing a challenge for healthcare settings. This review provides a comprehensive overview of hvKP, highlighting its structural and pathogenic differences from classical K. pneumoniae strains, key virulence factors, mechanisms of antibiotic resistance, and the increasing threat of multidrug-resistant hvKP. Lastly, we discuss current treatment guidelines and emerging therapeutic strategies to combat this formidable pathogen.

Itraconazole (ITZ) is a triazole antifungal agent characterized by broad-spectrum activity against fungal infections. The main drawback of ITZ, when applied topically, is the low skin permeability due to the stratum corneum, the outermost layer of the skin, which represents the main barrier for drug penetration. Therefore, this study aimed to prepare itraconazole as transferosomes (ITZ-TFS) to overcome the barrier function of the skin. ITZ-TFSs were prepared by thin lipid film hydration technique using different surfactants, sodium lauryl sulfate (SLS) and sodium deoxycholate (SDC). The prepared ITZ-TFS were evaluated for entrapment efficiency (EE) %, particle size, polydispersity index (PDI), zeta potential, and in vitro drug release to obtain an optimized formula. The surface morphology of the optimized formula of ITZ-TFS was determined by transmission electron microscope (TEM). The optimized formulation was prepared in the form of gel using hydroxyl propyl methyl cellulose (HPMC) gel base. The prepared ITZ-TFS gel was evaluated for homogeneity, drug content, spreadability, pH, and in vitro antifungal activity in comparison with the free ITZ gel. The prepared ITZ-TFS formulations exhibited high EE% ranging from 89.02 ± 1.65% to 98.17 ± 1.28% with particle size ranging from 132.6 ± 2.15 nm to 384.1 ± 3.46. The PDI for all ITZ-TFSs was less than 0.5 and had a negative zeta potential. The TEM image for the optimized formulation (ITZ-TFS4) showed spherical vesicles with a smooth surface. The prepared gels had good spreadability, pH, and acceptable drug content. ITZ-TFS gel showed higher antifungal activity than free ITZ gel as determined by zone of inhibition. ITZ was successfully prepared in form of TFSs with higher antifungal activity than the free drug.

The rising burden of fungal infections presents a significant challenge to global healthcare, particularly with increasing antifungal resistance limiting treatment efficacy. Early detection and timely intervention remain critical, yet fungal pathogens employ diverse mechanisms to evade host immunity and develop resistance, undermining existing therapeutic options. Limited antifungal options and rising resistance necessitate novel treatment strategies. This review provides a comprehensive overview of conventional antifungal agents, their mechanisms of action, and emerging resistance pathways. Furthermore, it highlights recently approved and investigational antifungal compounds while evaluating innovative approaches such as nanotechnology, drug repurposing, and immunotherapy. Addressing antifungal resistance requires a multifaceted strategy that integrates novel therapeutics, enhanced diagnostic tools, and future research efforts to develop sustainable and effective treatment solutions.

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.