Parkinson’s disease (PD) is considered the second most common neurodegenerative disease worldwide; treating this disease remains quite challenging. Environmental and genetic factors may play a role in the pathophysiology of PD. α-synuclein aggregation, oxidative stress, ferroptosis, mitochondrial failure, neuroinflammation, and gut dysbiosis are among the known risk factors of PD. The pathophysiology of Parkinson’s disease is complicated by the interconnections between these molecular pathways, which also present significant obstacles to treatment development. However, due to its complex mechanism and long latency, PD is difficult to diagnose and detect, which presents a barrier to treatment. In addition, the need to develop new treatments for PD is increased by the fact that the majority of traditional therapeutic methods have major side effects and limited effects. Therefore, a deeper understanding of the fundamental mechanisms underlying PD is required. This review provides a comprehensive analysis of the current landscape of PD pathophysiology, paying particular attention to the molecular processes of PD, as well as the traditional research models, clinical diagnostic standards, documented medication therapeutic approaches, and recently disclosed drug candidates in clinical trials. We also highlighted the herbal-derived components that have recently been identified for their effects in the treatment of PD to provide a review and perspectives for the development of the next generation of drugs and preparations for the treatment of PD.

Carbon dots (CDs), including carbon quantum dots (CQDs), are ultra-small carbon-based nanomaterials, typically below 10 nm, with tunable photoluminescence, high aqueous dispersibility, favorable biocompatibility, low toxicity, and abundant surface functional groups. These properties make CDs promising multifunctional platforms for nanomedicine, particularly in bioimaging, biosensing, targeted drug/gene delivery, photodynamic therapy (PDT), photothermal therapy (PTT), antimicrobial treatment, and theranostic applications. This review critically examines recent advances in CD fabrication, including top-down, bottom-up, green biomass-derived, microwave-assisted, hydrothermal, and emerging hybrid strategies, with emphasis on how precursor selection, heteroatom doping, surface passivation, and polymer/ligand functionalization regulate optical performance, biological interaction, and therapeutic efficiency. The review discusses structural classification, including CQDs, graphene quantum dots (GQDs), carbon nanodots, and carbonized polymer dots (CPDs), together with major characterization approaches such as ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM). Particular attention is given to red/near-infrared (NIR) emission, renal clearance, drug-loading behavior, reactive oxygen species (ROS) generation, toxicity mechanisms, biodistribution, and long-term biosafety. This review also highlights key translational barriers, including batch-to-batch variability, limited standardization, scalable manufacturing, regulatory uncertainty, and incomplete pharmacokinetic evaluation. It considers artificial intelligence (AI) and machine learning (ML) as emerging tools for reproducible CD design. CDs represent versatile and clinically promising nanoplatforms, but their translation requires standardized synthesis, rigorous safety assessment, and application-specific regulatory validation.

Major depressive disorder (MDD) is a mental illness with high mortality, suicide, and relapse rates that could become the leading cause of health problems worldwide by 2030. The microbiota–gut–brain axis involves bidirectional communication between the human gut microbiota and the central nervous system (CNS). The gut microbiome is a complex ecosystem of approximately 100 trillion microorganisms, including viruses, bacteria, and fungi. The gut microbiota has recently been recognized for its impact on various diseases and health concerns. Several factors influence the composition and structure of gut microbes, ultimately affecting human physiology, with the nervous system being particularly vulnerable. The gut–brain–microbiota axis influences several important brain functions through numerous pathways, including vagus nerve signaling, gut microbial synthesis of metabolites, and immune-related chemicals. These factors can influence neurotransmitter activity, neuroinflammation, behavior, and mental health. Despite increased interest, the possibility of modifying the gut microbiota as a therapeutic approach remains unclear. Although numerous studies suggest that microbiota play an important role in many illnesses, the precise mechanisms are yet to be elucidated, and there are currently no evidence-based, microbiota-focused treatments for these illnesses. Recent research indicates that gut dysbiosis (GD) causes increased intestinal permeability (leaky gut), initiates systemic inflammation, and contaminates the blood. Opportunistic microbial metabolites cross the blood–brain barrier, triggering a neuroinflammatory cascade and apoptotic pathways while affecting neurogenesis and neurotransmitters, ultimately resulting in the development of MDD and anxiety. This review examined the factors influencing normal gut microbiota and GD-mediated MDD, as well as possible therapeutic options. The study outlines its objectives and methodological approaches, including the screening and filtering of research on GD-induced depression. Furthermore, it explored the daily use of dietary supplements, revealing new paths for clinical and preclinical research.

Neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), arise from highly interconnected molecular and cellular abnormalities that progressively lead to neuronal dysfunction, synaptic failure, and cell death. This review provides a unified framework to understand the interrelated molecular mechanisms driving these diseases, with a focus on identifying key disease-specific intervention nodes. Core contributors include oxidative stress, mitochondrial dysfunction, protein aggregation, neuroinflammation, and emerging roles of peroxisomal dysfunction in redox imbalance, lipid dysregulation, and inflammatory amplification. Single-target therapies often show limited efficacy due to the complex, interconnected nature of these pathways. In contrast, polypharmacology, which targets multiple disease-relevant mechanisms simultaneously, offers a more promising therapeutic strategy. This review critically examines how pathway crosstalk drives neurodegenerative progression, with particular emphasis on mitochondrial–ROS–inflammatory signaling, aggregation–proteostasis failure, synaptic–neuroimmune dysfunction, and gut–brain communication. It evaluates various multi-node intervention strategies, including multi-target-directed ligands (MTDLs), molecular hybrids, natural products, drug repurposing, and nanocarrier-based delivery systems. Advances in network pharmacology, artificial intelligence (AI), bioinformatics, and multi-omics have enhanced the identification of actionable therapeutic nodes, candidate compounds, and brain-targeted delivery platforms. Notably, the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome and cyclic GMP–AMP synthase (cGAS)—stimulator of interferon genes (STING) pathways—play distinct roles in neuroinflammation, amplifying neuronal damage by releasing inflammatory cytokines and inducing mitochondrial dysfunction. However, successful translation into clinical practice remains constrained by challenges such as blood–brain barrier penetration, patient heterogeneity, and biomarker limitations. The review advocates for a shift towards mechanism-informed, patient-stratified polypharmacological strategies to better address the network pathology of neurodegeneration, despite significant translational hurdles.

Artificial intelligence (AI) is increasingly used in cancer research, enabling integrative analysis of complex biomedical data to identify actionable therapeutic vulnerabilities. This review specifically examines how AI advances mechanistic cancer target discovery and translational drug development, focusing on: (1) the processing of large-scale genomics, transcriptomics, proteomics, metabolomics, single-cell profiling, spatial, and clinical datasets using machine learning (ML) and deep learning (DL) algorithms; (2) the identification of candidate biomarkers, driver genes, dysregulated pathways, tumor dependencies, and molecular targets that traditional methods often miss; (3) the integration of multi-omics data, network biology, causal inference, and systems-level modeling to refine mechanistic understanding of cancer progression and separate functional driver events from passengers; and (4) applications in drug development, including virtual screening, molecular modeling, structure-informed target validation, drug repurposing, synthetic lethality prediction, and de novo drug design, which collectively may enhance early-stage drug discovery efficiency. The review underscores that AI serves as both a predictive tool and a platform for linking molecular mechanisms to hypothesis generation, target prioritization, and rational treatment design. Challenges such as data heterogeneity, algorithmic bias, interpretability, reproducibility, regulatory requirements, and patient privacy must be addressed for robust translation and clinical use. Future directions may focus on hybrid approaches that integrate causal modeling, explainable AI, multimodal data, and experimental validation to yield mechanistically grounded, clinically actionable insights. AI-driven approaches ultimately aim to accelerate mechanism-based cancer target discovery and enable more precise, biologically informed anticancer therapies.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and neuronal loss. The pathological hallmarks of AD include extracellular accumulation of amyloid-β (Aβ) plaques, intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein, chronic neuroinflammation, oxidative stress, mitochondrial dysfunction, and synaptic degeneration. Current symptomatic therapies provide modest clinical benefits, while recently approved amyloid-targeting monoclonal antibodies, such as lecanemab and donanemab, can slow decline in selected early-stage AD patients but do not cure the disease and are associated with safety, access, and cost concerns. This narrative review summarizes mechanistic evidence from in vitro and in vivo studies and distinguishes preclinical promise from validated clinical utility. Phytochemicals, including polyphenols, flavonoids, alkaloids, terpenoids, and carotenoids, demonstrate neuroprotective effects through antioxidant activity, anti-inflammatory modulation, inhibition of amyloid aggregation, regulation of tau phosphorylation, and support of mitochondria and synapses. Evidence from experimental models suggests that several phytochemicals may help slow AD pathology and improve cognitive function, but clinical translation remains limited due to poor bioavailability, inadequate blood–brain barrier (BBB) penetration, and a lack of large-scale clinical trials. This review highlights critical research gaps and emerging strategies to facilitate phytochemical-based preventive and therapeutic approaches in AD.