Search Results (51,354)

Conventional hazard and operability (HAZOP) studies remain central to process safety management, but their periodic, document-centered implementation and dependence on expert judgment limit their ability to track dynamic operational risk across the full plant lifecycle. This critical literature review, drawing on 113 sources, examines how digital twin (DT) and artificial intelligence (AI) technologies may augment HAZOP practice, advancing an augmentation principle: DT–AI should strengthen expert-led hazard analysis as a decision-support layer, not replace human judgment. The review maps conventional HAZOP limitations to four complementary technology pathways: AI-assisted knowledge capture and deviation reasoning; DT-based hazard monitoring and early warning; hybrid physics–data modeling for predictive safety; and explainable AI for operator trust and regulatory acceptance. Four representative industrial case studies illustrate current practice, and a structured barrier analysis identifies why many pilots stall before reaching sustained deployment. These pathways offer strong potential to improve HAZOP coverage, traceability, operational relevance, and lifecycle learning, but a study-level evidence assessment finds that no reviewed study demonstrates long-term, multi-site validation of an integrated DT–AI–HAZOP system; most implementations remain at conceptual, prototype, or limited pilot levels. Key failure modes, including model drift, sensor faults, large language model hallucination, and automation complacency, are examined alongside a thirteen-item implementation risk register spanning technical, organizational, economic, security, and scalability concerns. The review proposes a phased industrial implementation roadmap, a lifecycle-oriented safety-management framework, and a three-horizon research agenda for advancing toward reliable, validated HAZOP-informed digital twin systems. Full article

Background/Objectives: This study aimed to evaluate the impact of exercise on aerobic capacity in individuals with chronic obstructive pulmonary disease (COPD), and to explore potential exercise-related characteristics associated with greater improvements in aerobic capacity. Methods: Randomized controlled trials (RCTs) were systematically retrieved from Web of Science, Embase, Scopus, PubMed, and Cochrane Library, covering all records up to 13 May 2025. Studies were included if they were RCTs involving COPD patients and reported aerobic capacity outcomes. Methodological quality was evaluated using the Cochrane Risk of Bias tool, and effect sizes were synthesized using weighted mean differences (WMD) with 95% confidence intervals (CIs). Results: Thirty-seven studies were included, involving 1124 participants in exercise groups and 1089 in control groups. Exercise significantly improved aerobic capacity (WMD, 49.82; 95% CI: 38.54 to 61.10; p < 0.00001). Subgroup analyses revealed that certain exercise characteristics, including mixed exercise (WMD, 53.70; 95% CI: 41.58 to 65.83; p < 0.00001), sessions ≥ 60 min (WMD, 51.15; 95% CI: 37.53 to 64.76; p < 0.00001), ≤3 times weekly (WMD, 62.09; 95% CI: 47.49 to 76.68; p < 0.00001), total weekly volume ≤ 180 min (WMD, 59.08; 95% CI: 41.52 to 76.64; p < 0.00001), and supervised training (WMD, 56.25; 95% CI: 36.64 to 75.86; p < 0.00001), may be associated with larger improvements in aerobic capacity. Conclusions: Exercise is effective in improving aerobic capacity in COPD patients. Observed differences across exercise characteristics should be considered as exploratory and hypothesis-generating rather than definitive evidence of optimal exercise prescription. Future research should focus on investigating the long-term effects of exercise training on clinical outcomes (e.g., hospital readmissions, mortality) and exploring the efficacy of technology-assisted remote exercise programs to improve access to pulmonary rehabilitation services. Full article

Prefabricated construction is increasingly adopted worldwide because of its advantages in sustainability and construction efficiency. Nevertheless, the construction phase still faces substantial and multifaceted risks. These risks are characterized by high uncertainty and strong dependence on the subjective judgments of decision-makers, which may limit the wider promotion and application of prefabricated construction. Existing risk assessment methods often struggle to represent neutral and refusal attitudes in expert evaluations with sufficient precision. They also do not fully account for the psychological and behavioral mechanisms of decision-makers under risk. To address these limitations, this study proposes an integrated evaluation framework that combines interval-valued picture fuzzy sets (IVPFSs), cumulative prospect theory (CPT), and TOPSIS. The decision objective is to rank alternative construction-stage risk-control schemes and identify the relatively important risk factors influencing the ranking results. IVPFSs are used to represent support, opposition, neutrality, and refusal degrees in expert evaluations. CPT is incorporated to capture decision-makers’ risk preferences, reference dependence, and loss-aversion behavior. A comprehensive risk indicator system is developed from five dimensions: personnel, management, technology, environment, and materials/equipment. Attribute weights are determined using a coordinated AHP–entropy weighting scheme to balance subjective expert judgment with objective information contained in the evaluation data. The proposed framework is applied to a prefabricated building project in Shanghai. The results indicate that personnel-related factors, especially the technical proficiency of construction workers, are relatively important risk sources in the examined case. The model also provides a structured ranking of alternative risk-control schemes. Overall, this study offers an exploratory case-based decision-support framework for construction-stage risk management in prefabricated building projects and demonstrates the potential value of incorporating behavioral preferences into fuzzy multi-criteria risk assessment. Full article

Neurodegenerative diseases like Alzheimer’s disease (AD) and Parkinson’s disease (PD) are regarded as systemic illnesses, mainly characterized by long-term neuroinflammation, metabolic dysregulation and barrier dysfunction. All these factors interact through the gut–immune–brain axis. The Mediterranean diet, rich in plant-based foods, olive oil, and fish, has been consistently associated with slower cognitive decline and reduced risk of neurodegeneration in observational studies and some clinical trials. This review provides a systems-level synthesis that distinguishes itself from previous narratives by integrating disease-specific mechanistic frameworks with multi-omics-guided precision nutrition strategies. We summarize recent evidence showing that this dietary pattern can remodel gut microbiota composition and enhance the production of bioactive metabolites such as short-chain fatty acids (SCFAs). These metabolites are associated with improved intestinal barrier integrity, reduced systemic inflammation, and potential modulation of brain functions. Within the central nervous system, diet-related metabolites have been linked to reduced neuroinflammation via modulation of microglial and astrocytic states. They have been linked to protection of mitochondrial function, maintenance of proteostasis, and preservation of blood–brain barrier (BBB) integrity. In AD and PD patients, adherence to this diet is associated with favorable changes in pathological hallmarks, including amyloid-beta (Aβ), tau, and α-synuclein accumulation. Nowadays, multi-omics tools, including single-cell transcriptomics, spatial transcriptomics and microbiome analysis, are widely used in this field, which helps researchers explore these complicated effects more deeply. Importantly, individual responses to the diet vary considerably due to differences in genetic background, gut microbial composition, and metabolic phenotypes, which underscore the need to move from generalized dietary guidelines toward personalized precision nutrition. The Mediterranean diet is not only a dietary pattern but also an effective way to modulate neuro-immune and metabolic networks. However, current evidence remains largely observational, and we critically discuss the need for more randomized controlled trials (RCTs) and standardized multi-omics data analysis frameworks. To sum up, the Mediterranean diet plays a neuroprotective role via the gut–immune–brain axis, and multi-omics techniques promote the development of precision nutrition. More trials and improved multi-omics systems are required to apply these research results in clinical practice. Full article

DNA double-strand breaks (DSBs), caused by various endogenous and exogenous factors, pose a significant threat to genomic stability. Several conserved repair pathways address DSBs, with homologous recombination (HR) being the only mechanism capable of accurately restoring the original DNA sequence. The BRCA1 gene plays a critical role in HR and is involved in maintaining genomic stability, cell cycle regulation, transcription, and tumor angiogenesis. Germline mutations in BRCA1 are strongly associated with increased risks of breast, ovarian, and other cancers. Dysfunction of BRCA1 leads to homologous recombination deficiency (HRD), forcing cells to rely on error-prone repair pathways, which promotes genomic instability and tumorigenesis. Besides hereditary mutations, HRD can also arise in sporadic cancers through epigenetic mechanisms such as promoter hypermethylation and reduced BRCA1 expression. Although BRCA1 deficiency is uncommon in lung cancer, BRCA1 status is considered a potential biomarker for sensitivity to platinum-based chemotherapy and other cytotoxic agents used in lung cancer treatment. However, the impact of BRCA1 on treatment response and prognosis in lung cancer remains controversial and not fully understood. This review summarizes current evidence on the role of BRCA1 in modulating chemotherapy response and disease outcomes in lung cancer patients, highlighting its potential as a biomarker for personalized therapy selection. Thus, in this context, the key unresolved issues critical for the development of personalized treatment strategies for lung cancer associated with BRCA1 alterations include the identification of molecular biomarkers most reliably associated with tumor sensitivity to chemotherapy. In addition, the development of methods for identifying patients with homologous recombination deficiency specifically in lung tumors appears to be of considerable importance, as does a better understanding of how the biological and therapeutic implications of BRCA1-related parameters in lung cancer differ from those observed in other tumor types. Addressing these challenges could substantially improve the efficacy of chemotherapy and patient outcomes, while also expanding the opportunities for a personalized approach to treatment selection in patients with lung cancer. Full article

Objectives: Pancreatic cancer remains one of the most aggressive malignancies, characterized by late diagnosis, limited therapeutic options, and poor survival outcomes. Increasing evidence indicates that systemic inflammation and tumor microenvironment interactions play a crucial role in disease progression and patient prognosis. This review aims to summarize current evidence on the clinical utility of inflammation-based hematological indices derived from complete blood count (CBC), including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), in pancreatic cancer. Content: Available data consistently demonstrate that elevated NLR and PLR, as well as decreased LMR, are associated with poorer overall survival, more aggressive disease phenotype, and reduced response to therapy. Among these indices, NLR appears to be the most robust and widely validated prognostic marker. Its clinical value is enhanced when combined with markers such as CA 19-9. LMR reflects the balance between host immune response and monocyte-derived tumor-promoting activity, while PLR highlights the role of platelets in tumor progression, angiogenesis, and immune evasion. Despite their potential, the routine clinical implementation of these indices is limited by the lack of standardized cut-off values, variability between patient populations, and susceptibility to confounding factors such as inflammatory conditions. Summary and outlook: In this article we show that inflammation-based hematological indices represent inexpensive, accessible, and promising tools for laboratory diagnostics and prognostic assessment in pancreatic cancer. Their integration with clinical, biochemical, and molecular data may improve risk stratification and support personalized therapeutic strategies; however, further large-scale prospective studies are required to establish their standardized clinical use. Full article

Background: Oral potentially malignant disorders (OPMDs) have a complex, but not consistently consistent, risk of transformation to oral squamous cell carcinoma (OSCC). The histopathologic grading of dysplasia is the traditional means of prognosis although there is considerable inter-observer variability and the tool has poor predictive value. Thus, image-based AI-based methods such as computational pathomics (CP), clinical-photograph deep learning (CDL), and optical or spectroscopic image analysis via machine learning (ML) or deep learning (DL) have been proposed as potential non-invasive risk stratification methods. Aim of the review is to identify the current state of the evidence for AI applications in the field of image-based diagnosis and treatment of OPMDs, their methodological characteristics and predictive accuracy, and priorities for future research. Methods: the scoping review was conducted using Joanna Briggs Institute methodology and PRISMA-ScR guidelines. The PubMed/MEDLINE, Scopus, Web of Science and Embase databases were searched between January 2018 and March 2026. Inclusion criteria were studies that used quantitative image analysis, ML or DL to diagnose, prognosticate, or stratify risk of OPMD in OPMD cohorts. A pre-piloted form was used to extract data which were then synthesized descriptively. Results: the 423 records identified included 24 (16 primary image-based studies and 8 contextual reviews) that met the inclusion criteria. The majority of studies were retrospective (15/16), and computational pathomics (n = 10), clinical-photograph deep learning and optical/spectroscopic imaging (n = 4) were emphasized. There was no study that used the conventional radiologic radiomics (CT, MRI, CBCT, or PET/CT) in OPMD cohorts. Overall, predictive performance was good, with AUROC values ranging from 0.73 to 0.96 for the detection of malignant transformation, 0.94 to 0.97 for OPMD versus OSCC discrimination, and 0.90 to 0.97 for dysplasia grading. Only 44% contained external validation, only 6% were prospective, and only limited adherence was made to IBSI, TRIPOD-AI and CLAIM standards. Conclusions: AI-based image recognition for OPMDs shows good predictive performance, and it has yet to be developed to a mature stage of the process. Future multicentre study, image standardisation, external validation and better reporting standards are needed. Remarkably, radiologic radiomics is an important and unexplored research gap in OPMD risk prediction. Full article

Rapid and accurate identification of Candida species is critical for guiding antifungal therapy, especially with the emergence of intrinsically resistant pathogens. However, diagnostics using culture-based methods remain slow and labor-intensive, limiting timely treatment decisions. This systematic review evaluated the diagnostic performance and clinical applicability of nucleic acid amplification tests (NAATs) for Candida species identification using a PubMed search completed on 23 June 2025. A total of 888 records were screened, 333 full-text articles were assessed, and 158 studies were included based on criteria including comparison with standard diagnostic methods, diagnostic performance reporting, and involvement of clinical samples. PCR-based approaches were the most widely used, including conventional, nested, multiplex, real-time, and droplet digital PCR. Isothermal methods such as loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) were increasingly represented. Common molecular targets included the ITS and 18S/28S rDNA regions. The risk of bias assessment was completed with the QUADAS-2 tool. Diagnostic performance varied depending on assay design, specimen type, gene target, and reference method. Excellent specificity and low limits of detection were achieved, especially with isothermal platforms offering the shortest turnaround times and greatest potential for point-of-care implementation. Multiplex assays were particularly advantageous for detecting mixed-species samples, while highly specific assays were optimal for distinguishing clinically important species such as Candidozyma auris, Nakaseomyces glabratus, and Pichia kudriavzevii. Overall, NAATs represent a promising diagnostic tool for Candida species identification, but broader clinical adoption will require improved standardization, validation across diverse patient populations, and clearer interpretation of fungal burden in the context of colonization versus infection. Full article

Pollution by emerging contaminants like microplastic is one of the major environmental concerns. Microplastics have become ubiquitous anthropogenic pollutants of aquatic, terrestrial and atmospheric ecosystems, and can generate considerable ecological and health-related risks. Conventional remediation regimes are widely ineffective, due to the physicochemical recalcitrance of polymer matrices. Recent advances in microbial biotechnology have revealed several contrasting microbial taxa and enzyme systems, which can convert or mineralize synthetic polymers through a variety of pathways of complex biochemistry. This review summarizes the current understanding of microbial–polymer interactions, including surface colonization, biofilm-mediated depolymerization, and intracellular uptake of degradation intermediates. It also discusses recent developments in enzyme engineering, strain optimization employing the CRISPR method, and synthetic biology approaches improving catabolic efficiency. The advent of a variety of multi-omics technologies of metagenomics, transcriptomics, and metabolomics has enabled the characterization of novel hydrolases and oxidoreductases with a high potential catalytic efficiency. Advances in nanobiocatalysis, enzyme immobilization, and bioreactor technology improve the scale-up of these processes. Related molecular developments and environmental applications will promote the application of microbial biotechnology as a selective and sustainable tool for the mitigation of microplastic accumulation and the development of a circular bioeconomy that interacts positively with ecosystem resilience. Full article

Objectives: The facial nerve (FN) possesses one of the most intricate anatomical courses in the head and neck, traversing the brainstem, temporal bone, and parotid gland before terminating within the muscles of facial expression. Owing to its complex anatomy, marked anatomical variability, and frequent distortion by adjacent pathology, preservation of FN integrity remains a fundamental challenge in skull base, otologic, and head and neck surgery. This review aims to provide a comprehensive synthesis of the contemporary literature regarding the clinical anatomy of the FN and to examine how anatomical variation, pathology-induced distortion, surgical strategy, and emerging technologies influence nerve preservation and functional outcomes. Methods: A comprehensive narrative review of the literature was conducted using PubMed, Scopus, and Google Scholar. Publications from 1983 through 2026 were searched using combinations of keywords, including “facial nerve,” “facial nerve anatomy,” “anatomical variation,” “vestibular schwannoma,” “hemifacial spasm,” “parotid surgery,” “facial nerve injury,” “facial nerve reconstruction,” “facial reanimation,” “diffusion tensor imaging,” “intraoperative neurophysiological monitoring,” and “artificial intelligence.” Peer-reviewed anatomical, radiological, clinical, and review articles published in English were included, while conference abstracts and studies lacking direct anatomical or surgical relevance were excluded. Particular emphasis was placed on surgically relevant anatomical variations, pathology-related anatomical distortion, advanced imaging modalities, intraoperative neurophysiological monitoring, reconstructive techniques, and predictors of postoperative facial nerve function. Results: Facial nerve preservation was found to depend on the interplay between individual anatomical variability, disease-related anatomical distortion, and operative strategy. In vestibular schwannoma surgery, nerve displacement, capsular adhesion, and cystic tumor degeneration were consistently associated with increased surgical complexity and less favorable postoperative facial function. In hemifacial spasm, successful microvascular decompression relied on precise identification of neurovascular conflict at the root exit zone. Within the parotid gland, substantial variability in branching architecture and surgical landmarks contributed to an increased risk of iatrogenic injury. Advanced imaging techniques, particularly diffusion tensor imaging tractography, improved preoperative prediction of FN location, while intraoperative neurophysiological monitoring enabled real-time assessment of neural integrity and functional preservation. Emerging artificial intelligence-based predictive models demonstrated potential to enhance patient-specific surgical planning and prognostication. Conclusions: Contemporary facial nerve surgery has evolved toward an individualized, anatomy-driven, and function-preserving paradigm supported by advanced imaging, intraoperative monitoring, and reconstructive strategies. Detailed understanding of both normal FN anatomy and pathology-induced anatomical distortion remains essential for optimizing surgical decision-making, maximizing nerve preservation, and improving long-term functional outcomes. Future developments integrating multimodal imaging, predictive analytics, and artificial intelligence may further refine patient-specific management and enhance postoperative facial function. Full article

Iron overload is a recognized risk factor for female reproductive dysfunction, yet the underlying cellular and molecular mechanisms remain incompletely understood. In this study, the effects of iron overload on ovarian granulosa cells were investigated, and a protective role of the steroid biosynthesis pathway against ferroptosis was identified. A mouse model of ovarian iron overload was established by daily gavage of ferric citrate (FC, 120 mg/kg for 40 days). Iron-overloaded female mice exhibited disrupted estrous cycles, reduced serum estradiol levels, impaired antral follicle development, and decreased pregnancy rates and litter sizes. Metabolomic analysis of freshly isolated granulosa cells revealed significant depletion of unsaturated glycerophospholipids and fatty acids, along with reduced antioxidants such as glutathione, vitamin E, and coenzyme Q6, and enrichment of the ferroptosis pathway. Transcriptomic analysis showed marked upregulation of genes involved in steroid biosynthesis, including Hmgcr and Fdft1, and their master transcription factor Srebf2. In cultured KK1 granulosa cells, FC treatment increased intracellular Fe²⁺ and reactive oxygen species, decreased glutathione content and NADPH/NADP⁺ ratio, elevated malondialdehyde levels, and induced lipid peroxidation and plasma membrane rupture, all of which were attenuated by the iron chelator deferoxamine. Knockdown of Srebf2 suppressed Hmgcr and Fdft1 expression, exacerbated lipid peroxidation, and increased membrane damage in iron-overloaded cells, confirming that SREBF2-driven steroid biosynthesis acts as an endogenous anti-ferroptotic mechanism. Collectively, these findings demonstrate that iron overload triggers ferroptosis in granulosa cells, leading to follicular arrest and reduced fertility, and that activation of the steroid biosynthesis pathway counteracts ferroptosis, likely through the production of protective intermediates. This study provides a mechanistic basis for iron overload-induced female infertility and identifies the steroid biosynthesis pathway as a potential therapeutic target. Full article

Various Maillard reaction products (MRPs) are formed in infant formula during thermal processing and storage. However, safety assessments are often based on individual compounds, which may not adequately reflect the potential risks associated with exposure to multiple coexisting components in real food products. Therefore, this study used GC–MS to screen for typical MRPs in infant formula. Based on the detection results, glyoxal, 2-acetylfuran, 2-furfural, and 5-hydroxymethylfurfural (5-HMF) were selected to establish a mixed-exposure system. Subsequently, using zebrafish as a model, we systematically evaluated the toxic effects of two mixture concentrations, 1/9 maximum non-lethal concentration (MNLC) and 1/3 MNLC, on the immune system, nervous system, gastrointestinal tract, and liver. The results showed that these four MRPs coexisted in infant formula and that combined exposure induced significant biological damage even at low doses. Both mixture concentrations significantly reduced neutrophil and macrophage levels and promoted apoptosis in central nervous system cells. The higher-concentration mixture further reduced T-cell counts, suppressed motor neuron development, and decreased locomotor activity during the light phase. In addition, the higher-concentration mixture decreased the gastrointestinal area, increased the liver area, delayed yolk sac absorption, and caused marked histopathological damage. Compared with single-compound exposure, combined exposure exerted more pronounced effects on immune- and neuro-related endpoints, suggesting synergistic interactions among different MRPs and identifying the immune and nervous systems as the more sensitive targets of toxicity. In summary, mixed exposure to typical MRPs in infant formula may pose a greater biological risk than that predicted by evaluations based on individual components alone. This study provides experimental evidence for identifying combined toxicity and improving the safety risk assessment of MRPs in thermally processed infant foods. Full article

China’s 2026 land-use policy mandates that commercial and residential construction remain within existing urban footprints, making urban renewal the primary source of spatial capacity for future urban development. Understanding how diverse stakeholders make decisions regarding urban renewal and shape the resulting spatial configurations is therefore of critical importance. This study develops an integrated multi-agent system (MAS) and cellular automata (CA) model to simulate the spatial dynamics of urban renewal. The model explicitly incorporates the location preferences of three key agents—government, developers, and residents. Using Wuhan as a case study, we project land-use patterns for 2035 under three scenarios: a traditional development scenario, a multi-agent simulation (MAS) scenario, and an urban renewal scenario. The findings reveal that (1) urban renewal significantly improves land-use patterns by curbing urban expansion and reducing the loss of farmland and forest cover; (2) government agents exert the strongest driving force on construction land expansion (contribution coefficient: 0.1758), while residents demonstrate a discernible influence on the selection of renewal sites (0.1199), with developer preferences largely aligning with government priorities; (3) a marked preference divergence exists among agents—within central urban areas, resident selection probabilities are only 60–70% of those of government and developers, indicating a notable mismatch between market demand and planning objectives. Moreover, more than 74% of renewal areas are projected to generate economic returns below renewal costs, signaling a potential financial sustainability risk. These results suggest that while urban renewal can effectively slow urban sprawl and mitigate ecological land loss, it simultaneously necessitates multi-stakeholder governance frameworks and sustainable financing mechanisms to reconcile competing interests and reduce fiscal vulnerabilities. The proposed simulation framework offers quantitative support for spatial policy making in China’s future land stock-constrained urban development contexts. Full article

The rapid proliferation of the Internet of Things (IoT) has transformed industries by enabling seamless interconnectivity among devices, applications, and networks. However, this widespread adoption has also introduced significant security vulnerabilities, exposing IoT ecosystems to cyber threats such as unauthorized access, data breaches, and large-scale cyber-attacks. As IoT technology continues to evolve, mitigating these vulnerabilities remains a complex and pressing challenge. In this context, penetration testing, which is also known as pen testing, serves as a proactive security measure, enabling organizations to identify and address potential weaknesses before they can be exploited by malicious actors. Penetration testing for IoT systems is a specialized security assessment that addresses the unique vulnerabilities of interconnected devices, networks, and communication protocols, differing significantly from traditional computing and network penetration testing methodologies. In this regard, this study presents a review of penetration testing as a critical methodology for identifying, assessing, and mitigating security risks in IoT environments. We examine the key steps, tools, and methodologies specifically designed for IoT penetration testing, demonstrating their applicability across diverse infrastructures through a simple case study. Further, this study also proposes a novel Artificial Intelligence (AI)-enabled automated conceptual framework, AutoAIPenTest, that integrates machine learning, reinforcement learning, and large language models to perform intelligent, real-time security assessments in dynamic IoT ecosystems. Our findings highlight the critical role of proactive security measures, including structured penetration testing, secure development practices, and regulatory compliance, in strengthening the resilience of the IoT ecosystem. By discussing existing challenges and proposing effective security strategies, this study contributes to ongoing efforts to secure IoT domains and ensure that technological advancements do not come at the expense of cybersecurity. Full article

With the growing of the global population aging, the comorbidity of cardiovascular disease (CVD) and Alzheimer’s disease (AD) has gradually become a major contributor to global disease burden, with the two diseases exhibiting interacting pathological characteristics. This review summarizes the mechanisms underlying CVD-AD comorbidity, including cerebral hypoperfusion and protein aggregation, oxidative stress and inflammation, as well as metabolic disorders and genetic factors. We also assess the value of blood biomarkers in this comorbidity, such as p-tau217, NT-proBNP, NfL, GFAP, and miRNAs. We then review the role of exercise in ameliorating this comorbidity, integrating its potential mechanisms into five aspects: repairing cerebral hypoperfusion and endothelial injury, accelerating Aβ clearance and inhibiting Tau protein hyperphosphorylation, reducing inflammation, protecting neuronal structure and the blood–brain barrier, and improving metabolic disorder. We also analyze in detail the relationship between exercise targets and blood biomarkers. Finally, we discussed the impact and application of different exercise modalities in this comorbidity, concluding with suitable exercise prescriptions and relevant safety considerations for each modality, further organized the mainstream pharmacological and nutritional intervention strategies for CVD-AD comorbidity, and objectively explored their potential interactions when combined with exercise interventions. Future research should integrate multi-omics technologies and gradually refine clinical data on comorbidity, with the aim of developing more targeted, personalized exercise prescriptions for patients with comorbidities, thereby providing scientific theoretical guidance for early risk prevention and disease management in patients with co-occurring CVD and AD. Full article