Spinal cord injury (SCI) significantly impacts the central nervous system, with limited effective treatments available. Brain-derived neurotrophic factor (BDNF) plays a crucial role in neuronal growth, survival, and regeneration after SCI. MicroRNAs, particularly miR-124-3p, have been implicated in SCI pathophysiology. However, the relationship between miR-124-3p and BDNF in the context of SCI remains unclear. This study aimed to investigate the correlation between miR-124-3p expression and BDNF levels in a rat model of spinal cord injury and to assess how the timing of injury affects this relationship.

This study included 72 male Wistar rats divided into three groups: intact (n = 8), sham (n = 32), and SCI (n = 32). SCI diagnosis was confirmed through behavioral-motor function analysis using the Basso, Beattie & Brenham score and histological examination with crystal violet staining. The expression levels of miR-124-3p and BDNF were assessed using real-time polymerase chain reaction in all groups at four time points (one hour, one day, three days, and seven days post-injury).

In the SCI group, a marked reduction in miR-124-3p expression was observed relative to both the sham and intact groups. Conversely, there was a substantial elevation in BDNF expression within the SCI group in comparison to the sham and intact groups. The findings underscore a negative association between miR-124-3p expression and BDNF messenger RNA levels.

The downregulation of miR-124-3p and concurrent upregulation of BDNF suggest a potential regulatory role of miR-124-3p in modulating BDNF expression during SCI. These findings provide new insights into the molecular mechanisms underlying SCI and suggest that miR-124-3p and BDNF could serve as potential therapeutic targets. Further research is needed to explore the translational potential of these findings for developing novel diagnostic and therapeutic strategies for SCI.

In recent years, it has been found that Lycium barbarum can repair liver damage and promote liver regeneration. Additionally, the polysaccharides contained in Lycium barbarum have anticancer properties and can induce apoptosis in cancer cells. Molecular docking, a mature computer-aided method, is widely used in drug discovery. This study aimed to verify the efficacy of active ingredients of Lycium barbarum in the treatment of liver cancer by molecular docking.

The effect of the active ingredients of Lycium barbarum in the treatment of liver cancer was verified by molecular docking, based on a previous study that examined the impact of Lycium barbarum on liver cancer using network pharmacology.

The binding energies of the key active ingredients and core targets were all less than −5.0 kcal/mol (1 kcal = 4.184 J), with most of them being less than −7.0 kcal/mol. This indicates that the key active ingredients and core targets have good binding ability, with most demonstrating strong binding affinity.

Most of the active ingredients in wolfberry can spontaneously bind to the core target protein, thereby playing a therapeutic role in liver cancer.

Despite significant advances in Parkinson’s disease (PD) treatment, it remains incurable, with limited therapeutic options. Currently, repurposing already tested, safe drugs has emerged as an effective therapeutic strategy against various neurodegenerative diseases, including PD. Using a drug-repurposing approach, the current study investigated the neuroregenerative potential of polysialic acid mimicking compounds, 5-nonyloxytryptamine oxalate (5-NOT) and Epirubicin (Epi), an anti-cancer drug, in 1-methyl-4-phenylpyridinium (MPP+)-treated human neuroblastoma SH-SY5Y cells as a PD model.

The excitotoxic model was established by exposing SH-SY5Y cells to 500 µM of MPP+ and subsequently treating them with the test compounds. The effect of MPP+-induced toxicity on cellular and nuclear morphology, as well as on the expression of neuroplasticity and cell survival proteins, were studied by immunostaining, gelatin zymogram, and Western blot assays.

Treatment with 5-NOT and Epi significantly promoted the survival of MPP+-challenged SH-SY5Y cells and prevented changes in their cellular and nuclear morphology by regulating the expression of microtubule-associated protein (MAP-2) and polysialylated-neural cell adhesion molecule (PSA-NCAM) and NCAM synaptic plasticity proteins. Further, 5-NOT and Epi treatment also protected SH-SY5Y cells by restoring levels of nitric oxide, matrix metalloproteinase, and stress response proteins. Interstingly, 5-NOT attenuated MPP+-induced toxicity in SH-SY5Y cells by regulating the intrinsic protein kinase AKT/BAD apoptotic pathway and the P-38 MAP kinase synaptic plasticity pathway.

These preliminary findings suggest that 5-NOT, as a potential polysialic acid glycomimetic, may serve as a promising drug candidate for targeting neurodegeneration of dopaminergic neurons, a hallmark feature of PD.

Pituitary tumors are common intracranial neoplasms that can cause significant morbidity due to hormonal dysregulation and compression of surrounding structures. Despite advancements in surgical techniques, challenges persist in treating large, invasive, or recurrent tumors, where complete resection is often difficult. The molecular and genetic mechanisms underlying pituitary tumorigenesis are not yet fully understood, limiting the development of targeted therapies. This review provides a comprehensive overview of recent advancements in neuroendoscopic treatment of pituitary tumors, with a focus on pathogenesis, technological innovations, clinical outcomes, and future directions. We highlight the potential of neuroendoscopic surgery to improve patient outcomes while addressing persistent challenges, such as the steep learning curve and limitations in instrument maneuverability. Future research should prioritize enhancing instrument design, developing 3D and augmented reality visualization systems, and improving training programs to further advance neuroendoscopic techniques.

This review explores the integration of complexity science—specifically, the biological holographic phenomenon and chaos-fractal theory—with the fundamental principles of traditional Chinese medicine (TCM) to optimize perioperative recovery. It examines how these theories provide a scientific foundation for developing a digital TCM diagnosis and treatment platform. Key topics discussed include the application of digital four-diagnosis technology, visualization of perioperative Yin-Yang states, and artificial intelligence-driven biomarker discovery. By quantifying and digitizing core TCM concepts, this approach enables their incorporation into Enhanced Recovery After Surgery protocols. Ultimately, the review highlights the potential of integrating TCM with Western medicine to advance personalized postoperative management, offering both theoretical insights and practical strategies for improving perioperative care.

With the increasing use of artificial intelligence (AI) in diagnostics, AI algorithms have shown great potential in aiding diagnostics. As more of these algorithms are developed, there is overwhelming enthusiasm for implementing digital and artificial intelligence-based pathology (DAIP), but doubts and pitfalls are also emerging. However, few original or review articles address the limitations and practical aspects of implementing DAIP. In this review, we briefly examine the evidence related to the benefits and pitfalls of DAIP implementation and argue that DAIP is not suitable for every clinical laboratory.

We searched the PubMed database using the following keywords: “digital pathology,” “digital AI pathology,” and “AI pathology.”. Additionally, we incorporated personal experiences and manually searched related papers.

Ninety-two publications were found, of which 24 met the inclusion criteria. Many advantages of DAIP were discussed, including improved diagnostic accuracy and equity. However, several limitations of implementing DAIP exist, such as financial constraints, technical challenges, and legal/ethical concerns.

We found a generally favorable but cautious outlook for the implementation of DAIP in the pathology workflow. Many studies have reported promising outcomes in using AI for diagnosis and analysis; however, there are also several noteworthy limitations in implementing DAIP. Therefore, a balance between the benefits and pitfalls of DAIP must be thoroughly articulated and examined in light of the institution’s needs and goals before making the decision to implement DAIP. Approaches for mitigating machine learning biases were also proposed, and the adaptation and growth of the pathology profession were discussed in light of DAIP development and advances.