The spatial heterogeneity of tumors has long been a subject of significant interest in oncology. Recent research has revealed that tumors and their microenvironments undergo dynamic changes over time, particularly in the form of periodic circadian rhythms. Disruptions to these rhythms have been recognized as a pivotal factor in the advancement of tumorigenesis. Such disruptions not only induce dysregulation of gene expression within tumor cells, influencing tumor growth, metabolism, the cell cycle, and vascular homeostasis but also facilitate metastasis. Furthermore, they mediate the remodeling of the tumor immune microenvironment, fostering the development of an immunosuppressive milieu. Additionally, the in vivo metabolism and therapeutic responsiveness of tumor treatments—including chemotherapy, targeted therapy, and immunotherapy—have been shown to be modulated by circadian rhythms. This suggests that time-specific drug administration may enhance treatment efficacy, offering novel insights for precision cancer therapy. In this review, we systematically update contemporary research on the impact of circadian rhythms on tumor biology, encompassing both tumor progression and the efficacy of drug therapies. Building upon these insights, we explore the potential for a synergistic approach that integrates the targeting of rhythmic genes with current tumor treatment modalities. We also discuss the feasibility of tailoring tumor therapy to the rhythmic alterations that define in vivo metabolism and the efficacy of specific therapeutic agents, highlighting the significance of rhythm-based strategies in the personalized treatment of tumors and the prevention of associated diseases.

Artificial intelligence (AI) is profoundly transforming the paradigm of solid tumor drug development. By integrating multi-omics data, spatial transcriptomics, and advanced computational models, AI has significantly accelerated the discovery and validation of new targets, compressing the traditional ten-year research and development cycle to two to three years. Generative AI platforms have optimized small molecule inhibitors, biologics, and messenger RNA vaccines, achieving breakthroughs in overcoming tumor heterogeneity, improving efficacy, and predicting drug resistance. However, clinical translation still faces challenges such as data bias, algorithm transparency, and the validation gap between models and real-world human experience. This review aims to systematically elaborate on the transformative role of AI in solid tumor drug development and to promote interdisciplinary cooperation as well as the construction of ethical frameworks to enable the full realization of precision oncology.

Oxidative stress could be a key process in acyclovir (ACV)-induced nephrotoxicity. N-acetylcysteine (NAC) is a water-soluble antioxidant with anti-inflammatory activity. This study aimed to evaluate the protective effect of NAC on ACV-induced nephrotoxicity in adult Wistar rats.

Forty adult male Wistar rats (200–220 g) were used. The rats were randomly divided into eight groups (n = 5/group) and were treated intraperitoneally daily for seven days as follows: Group 1 (Control) was administered water (0.2mL), while groups 2–4 were administered NAC (25, 50, and 100 mg/kg). Group 5 was administered ACV (150 mg/kg), while groups 6–8 were supplemented with NAC (25, 50, and 100 mg/kg) prior to treatment with ACV (150 mg/kg). On day 8, the rats were weighed and euthanized, and blood samples were collected for the assessment of biochemical markers. The kidneys were weighed and subjected to oxidative stress markers and histological evaluations.

ACV had no significant (p > 0.05) effects on the body and kidney weights of rats compared to the control. ACV produced significant (p < 0.001) elevations in kidney malondialdehyde, serum urea, creatinine, and uric acid levels in rats, which differed from the control. There were significant (p < 0.001) decreases in kidney glutathione, superoxide dismutase, peroxidase, and catalase, as well as serum chloride, potassium, bicarbonate, and sodium levels in ACV-treated rats compared to the control. ACV caused widening of Bowman’s space and tubular necrosis in the kidneys of rats. Nonetheless, NAC supplementation abrogated ACV-induced nephrotoxicity in a dose-dependent manner. Kidney histology was restored by NAC supplementation.

NAC protected against ACV-induced nephrotoxicity. This finding shows that NAC may have therapeutic potential for nephrotoxicity caused by ACV.

The tumor microenvironment (TME) consists of a complex mix of cellular and non-cellular components, including immune cells, stromal cells, extracellular matrix, cytokines, and growth factors. These elements interact with tumor cells to influence tumorigenesis, growth, invasion, and metastasis. Long noncoding RNAs (lncRNAs)—a class of non-coding RNAs longer than 200 nucleotides—have attracted considerable attention for their roles in regulating gene expression at the epigenetic, transcriptional, and post-transcriptional levels. Emerging evidence suggests that lncRNAs are crucial in shaping the TME by modulating processes such as immune evasion, angiogenesis, metabolic reprogramming, and the maintenance of cancer stem cells. This review provides an overview of the current understanding of lncRNAs in the TME, focusing on their involvement in key signaling pathways and cellular interactions that drive tumor progression. We discussed how lncRNAs contribute to extracellular matrix remodeling, facilitate communication between tumor and stromal cells, and regulate immune cell infiltration and function within the TME. Additionally, we explore the potential of lncRNAs as biomarkers for early cancer detection and prognosis, as well as their promise as therapeutic targets to disrupt tumor-microenvironment crosstalk. The review also addresses challenges in targeting lncRNAs therapeutically, such as ensuring specificity, minimizing off-target effects, and achieving effective in vivo delivery of lncRNA-targeted therapies. Strategies to overcome these challenges include the development of highly specific lncRNA knockout technologies and the use of advanced delivery systems, such as nanoparticles and viral vectors, to precisely target tumor-associated cells. Overall, this review underscores the significant role of lncRNAs in the TME and their potential as novel tools for enhancing cancer diagnosis and treatment. By elucidating the multifaceted roles of lncRNAs in the TME, we aimed to provide insights that could lead to more effective, targeted therapeutic strategies, ultimately advancing cancer research and improving patient care.

Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is essential for non-invasively investigating brain function. However, conventional fMRI methods are limited by low spatial and temporal resolution. This narrative review evaluates recent advancements in deep learning techniques for high-resolution BOLD-fMRI reconstruction, focusing on super-resolution, segmentation, and image registration. A comprehensive literature search was conducted across PubMed, IEEE, Scopus, and Web of Science databases for the period 2000–2023. Studies employing deep learning methods, including convolutional neural networks, transformer-based models, and generative adversarial networks for super-resolution, segmentation, and registration of BOLD-fMRI, were included. Deep learning approaches demonstrated significant improvements in spatial resolution, segmentation accuracy, and registration robustness. Convolutional neural network-based models, particularly generative adversarial networks, notably improved image reconstruction quality and detail preservation. Preliminary studies targeting specific brain regions such as the cerebellum and hippocampus showed promise; however, systematic evaluations across broader brain areas and large-scale clinical validations remain limited. While deep learning techniques have led to substantial advancements in high-resolution BOLD-fMRI reconstruction, future research should focus on standardized protocols, multi-center validation, and improving computational efficiency and model generalization to enhance clinical utility.

Primary biliary cholangitis (PBC) is a chronic progressive autoimmune disorder characterized by small non-purulent intrahepatic bile duct destruction (ductopenia) and cholestasis. While the etiology of PBC remains unclear, it is believed to involve genetic-environmental interactions. Emerging evidence highlights gut microbiota dysbiosis in PBC patients, with increased symbiotic bacteria and decreased pathogenic bacteria. Microbial alterations potentially influence disease pathogenesis through multiple mechanisms, including immune dysregulation, intestinal barrier damage, BA metabolic dysregulation, and cholestasis. These findings suggest that the gut microbiota can serve not only as a non-invasive biomarker for diagnosis and prognosis evaluation but also as a therapeutic target for the disease. In this review, we summarize changes in PBC patients’ gut microbiota, explain how these changes affect disease occurrence and development, and discuss treatment methods with potential clinical value that intervene in gut microbiota.

Characteristic signs of alopecia are gradual thinning, disruption of structural features, and the hair development cycle (anagen, catagen, telogen) against the background of miniaturization of hair follicles, which leads to baldness and psychological stress in patients. Despite the rapid development and clinical application of synthetic pharmacological, cellular/acellular, and molecular drugs, no effective therapeutic agent against alopecia has yet been developed. Great hopes are pinned on the improvement of therapeutic strategies with the introduction of exosomes into practical application, which contain a wide array of active substances for the targeted stimulation of hair follicle activity (anagen inducers) through the regulation of intracellular signaling cascades, growth factors, and microRNAs. The review discusses in detail the microRNAs and their intracellular targets that control hair follicle morphogenesis. It also focuses on the prospects of using stem cell exosomes from various sources for the treatment of alopecia, providing a clinical rationale for potential benefits and risks.

Neurodegenerative diseases (NDs) represent a major global health challenge in aging populations, with their incidence continuing to rise worldwide. Although substantial progress has been made in elucidating the clinical features and molecular underpinnings of these disorders, the precise mechanisms driving neurodegeneration remain incompletely understood. This review examines the increasing significance of the gut–brain–immune triad in the pathogenesis of NDs, with particular attention to Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and multiple sclerosis. It explores how disruptions in gut microbiota composition and function influence neuroinflammation, blood–brain barrier integrity, and immune modulation through microbial-derived metabolites, including short-chain fatty acids, lipopolysaccharides, and bacterial amyloids. In both Alzheimer’s and Parkinson’s diseases, a reduced abundance of short-chain fatty acid-producing bacterial taxa has been consistently associated with heightened pro-inflammatory signaling, thereby facilitating disease progression. Although detailed mechanistic understanding remains limited, experimental evidence—primarily from rodent models—indicates that microbial metabolites derived from a dysbiotic gut may initiate or aggravate central nervous system dysfunctions, such as neuroinflammation, synaptic dysregulation, neuronal degeneration, and disruptions in neurotransmitter signaling via vagal, humoral, and immune-mediated pathways. The review further highlights how gut microbiota alterations in amyotrophic lateral sclerosis and multiple sclerosis contribute to dysregulated T cell polarization, glial cell activation, and central nervous system inflammation, implicating microbial factors in disease pathophysiology. In addition to identifying critical knowledge gaps, the review emphasizes the need for sustained, multifactorial research efforts, including the development of physiologically relevant brain–gut organoid models and the implementation of standardized experimental protocols. A major limitation in the field remains the difficulty of establishing causality, as clinical manifestations often arise after extended preclinical phases—lasting years or decades—during which aging, dietary patterns, pharmacological exposures, environmental factors, and comorbidities collectively modulate the gut microbiome. Finally, the review discusses how microbial influences on host epigenetic regulation may offer innovative avenues for modulating neuroimmune dynamics, underscoring the therapeutic potential of targeted microbiome-based interventions in neurodegenerative diseases.

Insulinoma is a neuroendocrine tumor originating in the pancreas that secretes excess amounts of insulin, leading to severe hypoglycemia. The clinical presentation of hypoglycemia is classically described by Whipple’s Triad. Due to the rarity of this diagnosis, it can often be mistaken for other etiologies with similar presentations. In this paper, we present the case of a woman in her 70s with metastatic insulinoma involving the liver, who was initially diagnosed with an insulin-like growth factor 2-secreting hepatocellular carcinoma. Biochemical and immunohistochemical analyses were instrumental in distinguishing between these two etiologies.