Rheumatoid Arthritis (RA) is a chronic auto-immune condition marked by swelling of synovial membrane, leading to joint discomfort, swelling, and the erosion of bone and cartilage. While the exact origin of rheumatoid arthritis remains uncertain, various medications such as nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, disease-modifying antirheumatic drugs (DMARDs), and biologic drugs have been utilized for its treatment. However, upon administration, these drugs distribute throughout the body, resulting in both wastage and potential side effects. Targeted drug delivery has been proposed to overcome the limitations of conventional therapy, while precision medicine is widely acknowledged to tailor treatments according to individual patient needs. This narrative review focuses on various advanced drug delivery strategies for management of RA, with a particular emphasis on active targeting approaches, stimuli responsive drug delivery and progressive integration of artificial intelligence in enabling precision diagnosis, treatment, and optimized therapeutic outcomes. Methods: An exhaustive literature review was conducted using PubMed, Scopus and Web of sciences databases on various targeted drug delivery strategies for management of RA. Mechanistically, folate, CD44, scavenger receptor or CD163 receptor targeting coupled with stimuli-responsive approach ensures targeted delivery of therapeutic agents, leading to reduction of TNF and IL-6 biomarkers. AI models, including deep learning, supervised and unsupervised machine learning models based on datasets, help in early detection of joint erosion, bone damage, and synovial inflammation more accurately than traditional methods. It helps to identify individuals at high risk of developing RA by analyzing multiple data sources including electronic health records (EHR) data, lab biomarkers, medication history, and clinical documentation. Supervised machine learning helps to predict disease severity and treatment responses. This allows for individualized dose adjustments and precision therapeutics. Further, AI enables treatment optimization based on each patient’s clinical profile. This narrative review concluded that various targeting strategies enhance the efficacy of therapeutic agents while the integration of AI in RA helps in early diagnosis, individualized treatment, and substantial precision in therapy. Furthermore, it emphasizes the need for future research to concentrate on creating versatile nanocarrier systems that can target multiple pathways related to RA simultaneously. Earlier reviews on RA dwelled only on targeting strategies for drug delivery and very few have focused on the AI integration in RA. Unlike those reviews, this review integrates three main approaches such as active targeting, stimuli-responsive delivery systems, and AI. It emphasizes how AI not only helps in the diagnosis of RA but it also helps in ligand selection, design of nanocarriers and therapy customization for precision therapy. Additionally, the prognostic outlook on personalized, data-driven AI-integrated nanocarriers for RA is provided.
Although the nutrient compositions of edible mushrooms are well-studied, the effect of combining different mushrooms on their anti-glycation and antioxidant activities remains unknown. This study therefore aimed to identify mushroom combinations that exhibit synergistic anti-glycation and antioxidant activities. Five edible mushroom species, namely Agaricus bisporus, Lentinula edodes, Pleurotus ostreatus, Pleurotus eryngii, and Flammulina velutipes, were evaluated both individually and in pairwise combinations. Their bioactive profile (phenolics, tannins, flavonoids, and polysaccharides), as well as antioxidant and anti-glycation activities were analyzed to determine the types of activity interaction: synergism, addition, or antagonism. A. bisporus (7.5 mg/mL) showed the highest reducing capacity and tannin content. L. edodes demonstrated the strongest radical scavenging potential, while F. velutipes displayed the highest anti-glycation activity and phenolic content. Despite its high polysaccharide level, P. eryngii showed low antioxidant activity. Pairwise combinations revealed synergistic anti-glycation and antioxidant effects at low sample concentrations, while antagonistic anti-glycation and antioxidant effects were observed at high sample concentrations. The mushrooms' polyphenols, tannins, and flavonoids were positively correlated with their antioxidant activity (r = 0.325 to 0.825, p < 0.05). However, they showed an inverse relationship (r = -0.349 to-0.644, p < 0.05) with polysaccharides and anti-glycation activity. The principal component analysis revealed that the types of bioactive content and mushroom combinations contributed to respective 53 and 23% of total activity variances. The best-performing mushroom combinations with synergistic anti-glycation and antioxidant activities were the mixtures of 7.5 mg/mL A. bisporus + 15 mg/mL F. velutipes, 7.5 mg/mL L. edodes + 7.5 mg/mL F. velutipes, and 7.5 mg/mL L. edodes + 15 mg/mL F. velutipes.
Pain is common in cancer patients, and its management relies heavily on opioid analgesics, yet local drug utilization data in developing countries like Myanmar are scarce. This study aimed to determine the drug utilization pattern of opioid analgesics in patients with cancer pain attending the Radiation Oncology and Medical Oncology Departments of Yangon General Hospital. This hospital-based cross-sectional study analyzed data from 2076 cancer patients (818 inpatients, 1258 outpatients) who received at least one opioid for pain within the 3-month study period for each oncology ward. Opioid utilization was quantified using the standard Anatomical Therapeutic Chemical/Defined Daily Dose (ATC/DDD) methodology, and Drug Utilization 90
Alzheimer’s disease (AD) is increasingly recognized as a disorder of dysregulated neuroimmune connectivity rather than isolated proteinopathy. The immuno-glial connectome, the dynamic interplay between microglia, astrocytes, and peripheral immune systems, constitutes a central driver of disease initiation and progression. Emerging single-cell and spatial transcriptomic studies reveal heterogeneous glial subpopulations with context-dependent transcriptional programs governed by TREM2–APOE, NF-κB, JAK/STAT, and NLRP3 inflammasome signaling. These networks converge to sustain chronic inflammation, impair amyloid-β clearance, and accelerate tau pathology. Complement dysregulation (C1q–C3 axis) further promotes aberrant synaptic pruning, while cytokine feedback loops involving IL-1β, TNF-α, and IFN-γ amplify neurotoxicity. Beyond the brain, peripheral immune cells, monocytes, macrophages, T and B lymphocytes, and neutrophils breach the compromised blood–brain barrier (BBB), perpetuating inflammatory cascades. Parallelly, gut dysbiosis and microbial metabolites modulate microglial reactivity via the gut–brain axis (GBA), linking systemic inflammation to central immune activation. Recent advances in plasma and cerebrospinal biomarkers (GFAP, sTREM2, YKL-40, and neurofilament light chain) enable in vivo tracking of neuroinflammatory dynamics, bridging mechanistic research with clinical translation. Therapeutic strategies targeting the immuno-glial interface, including selective NLRP3 inhibitors, TREM2 agonists, anti-cytokine biologics, and microbiome modulation, are reshaping the therapeutic landscape. Framed through the concept of an immune–glial connectome, this review synthesizes how coordinated interactions among microglia, astrocytes, and peripheral immune cells converge to drive synaptic dysfunction, circuit-level disintegration, and cognitive decline in neurodegenerative disease, particularly in AD. An immuno-glial network in AD, where central glia, peripheral immune cells, and the gut–brain axis interact through cytokines, oxidative stress, and barrier dysfunction. These interrelated pathways amplify inflammation via NF-κB, JAK/STAT, and NLRP3 signaling, linking immune dysregulation to neurodegeneration.
