MedComm - Future medicine最新文献

Mixed reality is an immersive visualization technology based on simultaneous localization and mapping systems and standalone head-mounted displays. It enables seamless integration and dynamic interaction among users, virtual elements, and the physical environment. Although numerous clinical studies and in vitro experiments have confirmed the value of mixed reality in surgical practice, the hierarchy of evidence remains limited. This review draws on published English-language literature to summarize its technical foundations, clinical applications, current innovations, and existing challenges. Specifically, the primary procedures of mixed reality-assisted surgery consist of three-dimensional reconstruction, holographic visualization, and spatial registration. Its clinical applications span preoperative planning, intraoperative navigation, surgical training, and postoperative rehabilitation. However, current limitations include insufficient computational and display capabilities of head-mounted displays, inadequate accuracy in spatial registration, high costs, workflow complexity, and unresolved ethical concerns. Therefore, we recommend increased resource allocation for technological innovations, multicenter randomized controlled clinical trials, and detailed risk-benefit assessments, aiming to establish and validate standardized clinical workflows. As the first comprehensive narrative review to compare the clinical applicability of mixed reality across all surgical specialties, this article outlines future research directions by analyzing representative clinical studies and offers a reliable report on current progress.

Air pollution poses a significant threat to respiratory health globally, contributing to the development and worsening of diseases, such as asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, and lung cancer. A key mechanism behind this involves epigenetic reprogramming, where environmental exposures alter gene activity without changing the underlying DNA sequence itself. This review deciphers the multilayered epigenetic mechanisms linking pollutants (e.g., PM_2.5, cigarette smoke, and ozone) to respiratory pathology, emphasizing reversible modifications that bridge environmental exposure and disease phenotypes. Air pollution-induced epigenetic reprogramming regulates critical biological processes, such as immune imbalance, chronic inflammation, oxidative stress, cellular dysfunction (senescence, apoptosis, and ferroptosis), tissue remodeling (epithelial–mesenchymal transition and fibrosis), and genomic instability. Specifically, aberrant DNA methylation, dysregulated RNA methylation, perturbed noncoding RNA networks, and histone modification abnormalities collectively drive disease pathogenesis. Furthermore, emerging epigenetic therapies targeting these modifications, such as DNA methyltransferase inhibitors (5-AZADC), histone deacetylase inhibitors, and RNA methylation regulators (methyltransferase-like 3 inhibitor STM2457), show promising therapeutic potential. This review highlights the reversibility of epigenetic changes as a strategic basis for intervention, emphasizing the need for future research on mixed exposures, tissue-specific mechanisms, and clinical translation to mitigate the global burden of pollution-related respiratory diseases.

Traditional Chinese Medicine (TCM), with its fragmented knowledge system, subjective diagnostics, and limited standardization, faces ongoing challenges in modernization. The rapid development of Large Language Models (LLMs) offers an unprecedented opportunity for the digitalization and standardization of TCM knowledge. By leveraging deep semantic understanding, contextual reasoning, and knowledge-graph–based inference, LLMs can systematize knowledge, integrate multimodal data, and support both standardized and personalized decision-making. Several TCM–LLMs, such as Qibo and MedChatZH, have been developed. Although previous reviews compared individual TCM–LLMs, a comprehensive analysis of common characteristics is lacking. This review addresses this gap by examining the “construction–application–challenges–prospects” paradigm of TCM–LLMs. Construction generally involves collecting authoritative data from diverse sources, standardizing textual content, training and fine-tuning on foundational LLMs, and subsequent evaluation. Key applications include auxiliary diagnosis and treatment, health management, medical education, and drug discovery. Five major challenges are data quality, reasoning performance, multimodal integration, ethical and regulatory compliance, and cultural adaptability. On the basis of this analysis, we propose a classification scheme that categorizes applications into four types and adopt the identified challenges as evaluation dimensions for future TCM–LLMs. This framework aims to clarify conceptual structures, guide methodology, and provide a foundation for innovation and cross-cultural integration in TCM research.

Urological cancers represent a significant and growing global health challenge. This study aims to provide a comprehensive assessment of the global burden, trends, and inequalities of prostate, bladder, and kidney cancers from 1990 to 2021, and to project their future burden to 2050. Utilizing data from the Global Burden of Disease Study 2021, we analyzed incidence, prevalence, mortality, and disability-adjusted life years (DALYs). We employed joinpoint regression to analyze temporal trends, frontier analysis to evaluate healthcare efficiency, and Bayesian age-period-cohort models to project future burden. Risk factor attributions were also quantified. In 2021, prostate cancer demonstrated the highest age-standardized rates across all metrics. From 1990 to 2021, age-standardized mortality and DALY rates declined for all three cancers. The burden was disproportionately higher among males and older populations, with smoking, high fasting plasma glucose, and high body-mass index identified as leading risk factors. Projections to 2050 indicate a continued decline in all age-standardized rates globally. Despite favorable trends in standardized rates, persistent sex disparities, growing absolute case numbers due to population aging, and the influence of modifiable risk factors necessitate targeted public health interventions and strategic healthcare planning.

The long-term survival benefit of neoadjuvant therapy (NAT) in breast cancer patients eligible for breast-conserving surgery (BCS) remains uncertain. This retrospective cohort study analyzed 94,677 BCS-eligible patients from the SEER database (2010–2020), including 8565 who received NAT. After propensity score matching (n = 5734 each), NAT significantly improved overall survival (OS) only in patients with triple-negative (HR = 0.79), ER-negative (HR = 0.80), and stage IIA (HR = 0.81) disease. No OS benefit was observed in HER2-positive patients despite high response rates. To guide treatment decisions, two machine learning models using Random Survival Forest were developed to predict 5-year OS, showing good discrimination (C-index: 0.743 for BCS, 0.690 for NAT-BCS). SHAP analysis identified age, stage, and breast subtype as key prognostic factors. Cross-stratification based on predicted OS revealed that 8.9% of BCS patients could benefit from NAT, while 90.8% of NAT-BCS patients might safely omit it. Patients whose treatment matched model recommendations had significantly better survival. These findings suggest that NAT provides limited survival benefit in BCS-eligible patients, with the advantage concentrated in specific subgroups. Predictive modeling offers a clinically useful approach to personalize NAT use, potentially reducing unnecessary treatment while identifying those most likely to benefit.

In the recent article published in Cell [1], Kaltenecker et al. systematically uncovered the liver as an active pathological driver of cancer cachexia (CCx). Through integrated multi-omics analysis, the study identifies the downregulation of the circadian regulator REV-ERBα (nuclear receptor subfamily 1 group D member 1, NR1D1) as a central mechanism that disrupts hepatic homeostasis and induces abnormal secretion of hepatokines, thereby promoting peripheral tissue wasting. These findings provide a conceptual framework for developing therapeutic strategies targeting the “liver–peripheral tissue” axis in CCx.

Cancer cachexia is a highly catabolic metabolic syndrome associated with advanced malignancies, characterized by progressive weight loss, skeletal muscle atrophy, and adipose tissue degradation [2, 3]. It is particularly prevalent in pancreatic, gastric, and lung cancers, affecting 50%–80% of patients and accounting for approximately 20% of cancer-related deaths. While previous studies have predominantly focused on local mechanisms of muscle atrophy, such as the ubiquitin-proteasome system (UPS), autophagy, and the NF-κB or FOXO pathways, the systemic metabolic functions of the liver in CCx pathogenesis remain underexplored. Notably, a concurrent study by Garrett et al. published in Cell demonstrated that tumor-driven inflammation can alter the vagal nerve–liver axis, resulting in suppression of hepatic HNF4α and exacerbation of systemic catabolism, further highlighting the critical involvement of the liver in this condition [4].

Given the pivotal role of the liver in systemic metabolism, the authors hypothesized that tissue wasting in cachexia may originate from hepatic responses to tumor-derived signals, which drive peripheral tissue catabolism through the secretion of specific factors. To investigate this, Kaltenecker et al. performed integrated transcriptomic and epigenomic profiling of hepatocytes from both weight-stable and cachectic cancer mouse models. These analyses uncovered a distinct transcriptional program specific to cachexia, characterized by dysregulated expression of the circadian regulator REV-ERBα. Hepatic restoration of REV-ERBα expression markedly attenuated both muscle and fat wasting. Mechanistically, REV-ERBα modulates tissue degradation by controlling hepatokines that activate catabolic pathways in myotubes and adipocytes. Notably, clinical data further revealed elevated circulating levels of key hepatokines in cachectic patients, independent of classical inflammatory cytokines such as IL-6 or TNF-α, underscoring a novel “hepatokine-peripheral tissue” axis in CCx pathophysiology.

Delving further into molecular effectors, the authors identified three key liver-secreted proteins-lipopolysaccharide-binding protein (LBP), inter-α-trypsin inhibitor heavy-chain H3 (ITIH3), and insulin-like-growth-factor-binding protein 1 (IGFBP1)–as direct targets of REV-ERB

Tumor immunogenicity determines their response to immune checkpoint inhibitors (ICIs), but the mechanisms governing pancancer immunogenicity remain incompletely understood. A further critical barrier to developing reliable predictive biomarkers is data set shift, which undermines model generalizability. Here, we address these challenges by developing a novel adversarial validation (AV)-integrated machine learning framework, focusing on immunogenic cell death (ICD)-related gene signatures (ICDRSs). We designed three AV-based strategies to mitigate data set shift and validate the efficacies across multiple machine learning algorithms. Using dual-modal data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), four optimal AV-based classifiers (e.g., GradientBoosting, XGBoost, LGBM, and CatBoost) were screened, which effectively reduced inter-cohort shift, enhancing both accuracy and robustness of downstream analysis. We identified novel risk/protective ICDRSs that strongly predicted patient survival and tumor immunogenicity across cancers. High-risk ICDRSs correlated with immune-exclusive microenvironments marked by impaired antigen presentation and aberrant tumor-associated macrophage development, as revealed by single-cell RNA sequencing. Validation across 13 ICI-treated cohorts revealed the capacity of ICDRSs for anti-PD-1 nonresponse. Mechanistically, risk ICDRSs were linked to CD47-SIRPA-mediated immune evasion and proliferative macrophage subsets with terminal dysfunction. This study advances understanding of tumor immunogenicity, provides novel biomarker development tools, and supports personalized cancer immunotherapy decision-making.