MedComm - Future medicine最新文献

Mitochondria are the primary energy hubs of cells and are critical for maintaining cellular functions. However, aging leads to a decline in mitochondrial efficiency. This decline is marked by increased reactive oxygen species, accumulation of mitochondrial DNA mutations, impaired oxidative phosphorylation, and breakdown of mitochondrial quality control systems. Such changes are associated with the development of neurodegenerative, cardiovascular, and metabolic diseases. Although much research has been done, the precise connection between mitochondrial dysfunction and aging remains unclear. Furthermore, current literature exhibits a lack of systematic organization regarding the mitochondria-targeted therapeutic interventions. This review systematically explores the mechanisms underlying mitochondrial deterioration during aging. Key focuses include impaired biogenesis, disrupted dynamics, dysregulated stress responses, and defective clearance of damaged mitochondria. Additionally, this review explores innovative therapeutic strategies for these mitochondrial problems, including a combination of nanodelivery systems, artificially intelligent drug-screening techniques, and cutting-edge tools, such as CRISPR/Cas9 gene editing. By integrating recent advances in mitochondrial biology, this review provides a comprehensive framework that bridges basic mechanisms with clinical applications. The insights presented here underscore the potential of precision mitochondrial medicine as a novel approach to combating age-related disorders, enhancing our capacity to address age-related diseases, and foster healthy aging.

Gemini surfactants (GSs) are two single-chain surfactant molecules covalently linked to their hydrophilic head groups via a spacer, resulting in a distinct structure with two hydrophilic heads and two hydrophobic tails. The GSs with cationic head groups have the potential for gene delivery by forming aggregates with negatively charged nucleic acids under the action of positive charge and self-assembly ability. Therefore, they have attracted increasing attention in the field of gene delivery. However, there remains a lack of systematic reviews summarizing various optimization strategies for GSs as gene delivery vectors in recent years. To address this gap, this review summarizes strategies for enhancing the transfection efficiency and biocompatibility of Gemini surfactant vectors, explores the relationship between their molecular structure and gene delivery performance, along with their delivery mechanism, highlights their applications in various gene delivery contexts, and discusses future development strategies and key challenges. This review provides a foundation for the further development of superior GSs, offering additional viable approaches for effective gene delivery and gene therapy of diseases.

Immune checkpoint inhibitors (ICIs) have transformed cancer immunotherapy, but their clinical efficacy varies significantly due to tumor heterogeneity and patient-specific factors. Existing databases lack comprehensive integration of ICI efficacy data and fail to explore biomarkers across pan-cancer contexts, limiting their utility in precision oncology. To address this gap, we developed ImmunoCheckDB, a systematic platform that curates 173 studies on cancer ICI treatment, integrating survival outcomes for traditional and network meta-analyses with multiomic data sets from public repositories, including over 93,000+ individuals across 18 cancer types and 30 ICI regimens to provide a robust resource for pan-cancer biomarker discovery. Equipped with online tools for meta-analysis, network meta-analysis, and multiomic profiling, ImmunoCheckDB enables researchers to investigate correlations between ICI efficacy and molecular biomarkers, featuring key functionalities such as real-time visualization of forest plots, funnel plots, and network diagrams, as well as association analyses linking multiomic data to clinical outcomes. Uniquely combining meta-analytical with multiomic exploration, our platform offers insights into optimal patient populations for ICI therapy, thereby bridging the gap between clinical data and molecular research to empower researchers in advancing precision immunotherapy, with access available at https://smuonco.shinyapps.io/ImmunoCheckDB/ to democratize data-driven insights for personalized cancer treatment.

Recently, a study by Péter Nagy's team [1] published in Cell Metabolism identified that the upregulation of cystathionine γ-lyase (CSE) and the inhibition of cystathionine β-synthase (CBS) are key factors contributing to the development of resistance to B-Raf proto-oncogene, serine/threonine kinase (BRAF) inhibitors (BRAFi) in treatment. Coadministration of the CSE inhibitor d,l-propargylglycine (PAG) with BRAFi significantly improved therapeutic efficacy and delayed the onset of resistance, offering a novel therapeutic strategy for patients with BRAF V600E-mutant (a valine-to-glutamic acid substitution at position 600 in the BRAF protein) melanoma.

Malignant melanoma is a highly aggressive tumor originating from melanocytes, with approximately 50% of patients harboring the BRAF V600E mutation [1]. This mutation leads to the activation of the downstream mitogen-activated protein kinase kinase/extracellular-signal-regulated kinase (MEK/ERK) signaling pathway, which in turn results in an increase in aerobic glycolysis, thereby supporting the proliferation of melanoma cells [2]. BRAF V600E inhibitors, such as vemurafenib (V) and dabrafenib (D), have been approved by the Food and Drug Administration (FDA) for the treatment of melanoma. However, resistance to these therapies often develops. Even when combining dabrafenib with the MEK inhibitor trametinib, resistance remains an inevitable challenge [3].

Existing research has indicated that treatment with dabrafenib-trametinib (DT) inhibits the BRAF/MEK/ERK pathway, leading to a shift in melanoma cell metabolism from aerobic glycolysis to mitochondrial respiration, which is unfavorable for melanoma cell proliferation [4]. Concurrently, the increased mitochondrial oxidative phosphorylation and electron transport chain (ETC) pathways result in enhanced reactive oxygen species (ROS) production. Excessive ROS production can disrupt the cellular redox balance and trigger oxidative stress responses. Building on this, the study discovered significant expression of cytochrome P (CYP)450 enzymes in dabrafenib- and trametinib-treated cells (DTC), with upregulation of CYP1B1 and CYP2F1 in dabrafenib- and trametinib-double resistant cells (DTR). These enzymes contribute to ROS production. To counteract the effects of ROS accumulation, antioxidant enzymes, such as superoxide dismutase 2 (SOD2), thioredoxin reductase 1 (TrxR1), catalase, 14-kDa human thioredoxin (Trx)-related protein (TRP14), glucose-6-phosphate dehydrogenase (G6PD), and glutathione (GSH) peroxidase 1 and 4 (GPX1, 4), are upregulated in DTC. However, this antioxidant response is limited, and the cells become more sensitive to exogenous oxidants, making them more susceptible to oxidative stress-induced damage. To support the function of antioxidant enzymes, DTC increasingly rely on the pentose phosphate pathway (PPP) to generate more nicoti

The skin, the largest organ in the human body, serves both as a mechanical barrier and an autonomous lymphoid organ, protecting against various environmental threats while maintaining the balance and functionality of multiple bodily systems. This relationship extends beyond the skin itself, involving other organs closely linked to skin homeostasis and related diseases. However, systematic reviews in this area are still lacking. This review seeks to explore this bidirectional communication, with a particular focus on the critical role of the immune system. We present a comprehensive review of the latest evidence, highlighting the fundamental roles of immune cells and cytokines within the skin–organ axis, particularly IL-17A, which appears to interact with nearly all organs, illustrating their interplay and impact on skin health. Additionally, we discuss the implications of these interactions for the design and application of skin-on-a-chip and organ-on-a-chip technologies, emphasizing the importance of understanding these relationships for developing physiologically relevant in vitro models. By providing a comprehensive analysis of these complex interactions, this review establishes a solid theoretical foundation for the prevention, diagnosis, and treatment of diseases associated with the skin–organ axis, particularly regarding immune cells, cytokines, microorganisms, and their metabolites, ultimately aiming to advance research in related fields and offer new insights for clinical applications.

DeepSeek-R1 is an open-source Large Language Model (LLM) with advanced reasoning capabilities. It has gained significant attention for its impressive advantages including low costs and visualized reasoning steps. Recent advancements in reasoning LLMs like ChatGPT-o1 have significantly exhibited their considerable reasoning potential, but the closed-source nature of existing models limits customization and transparency, presenting substantial barriers to their integration into healthcare systems. This gap motivates the exploration of DeepSeek-R1 in the medical field. Thus, we comprehensively review the transformative potential, applications, and challenges of DeepSeek-R1 in healthcare. Specifically, we investigate how DeepSeek-R1 can enhance clinical decision support, patient engagement, and medical education to help for clinic, outpatient and medical research. Furthermore, we critically evaluate challenges including modality limitations (text-only), hallucination risks, and ethical issues, particularly related to patient autonomy and safety-focused recommendations. By assessing DeepSeek-R1′s integration potential, this perspective highlights promising opportunities for advancing medical AI while emphasizing necessary improvements to maximize clinical reliability and ethical compliance. This paper provides valuable guidance for future research directions and elucidates practical application scenarios for DeepSeek-R1′s successful integration into healthcare settings.

The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines.

Artificial intelligence (AI)-driven learning is transforming education, requiring educators to quickly develop the skills to integrate AI tools effectively so they complement rather than replace traditional teaching practices. The fast pace of generative AI development poses challenges, particularly for less tech-savvy teachers or those who delay learning about these tools, leaving them at risk of falling behind. This is further compounded by students' quick adaptation to widely available models such as ChatGPT-3.5 and Deepseek R1, which they increasingly use for learning, assignments, and assessments. Despite existing discussions on AI in education, there is a lack of practical guidance on how medical educators can effectively and responsibly implement AI tools in teaching. This perspective provides a practical guide for medical educators to effectively incorporate AI tools to complement their teaching strategies, generate student assessments and to adapt assignments suitable for the AI era. We address challenges such as data bias, accuracy, and ethics, ensuring AI enhances rather than undermines medical training when aligned with sound pedagogical principles. This review provides a practical, structured approach for educators, offering clear recommendations to help bridge the gap between AI advancements and effective teaching methodologies in medical education.