Accurate kidney ultrasound segmentation is fundamental for clinical measurement and computer-aided diagnosis. However, domain shifts across devices and centers-manifested as differences in grayscale intensity, contrast, and speckle texture statistics-can substantially degrade model generalization, while acquiring new pixel-level annotations is costly. To address this, we propose a statistical spectral-similarity-guided ultrasound-to-ultrasound translation method to improve kidney segmentation performance without target-domain annotations. Motivated by frequency-domain analysis of renal ultrasound data, we observe that mid-to-low frequency components, which encode global organ structure, exhibit high consistency across domains, whereas mid-to-high frequency components, dominated by device-dependent speckle and texture statistics, vary substantially. Based on dataset-level frequency statistics, our method automatically identifies spectrally similar frequency bands shared by the source and target domains and derives structural guidance from them. This guidance is injected as a soft condition throughout a diffusion-based image generation process, enabling translation to target-device appearance while preserving anatomical structure. The translated images, paired with source-domain labels, are then used to train a segmentation network without requiring any target-domain annotations. Experiments on two public renal ultrasound datasets (OKUS and UNK) and an in-house multi-center dataset demonstrate superior structural preservation in image translation and consistently improved downstream segmentation performance, with particularly large reductions in boundary error. In the challenging OKUS to UNK adaptation scenario, our method boosts the mean Dice score by up to 20.52% (from 56.05% to 76.57%) and drastically reduces the 95% Hausdorff Distance (HD95) boundary error by 71.96 mm compared to the direct transfer baseline. Furthermore, consistent performance gains are achieved across the in-house multi-center dataset. These results indicate that the proposed spectral-similarity-based guidance effectively handles ultrasound domain shifts, substantially improving robustness and generalization for kidney segmentation under zero-shot and cross-center settings.
Conservation of post-translational modifications (PTMs) in histones across six plant species.
The use of advanced analytics in public health policy remains hindered by a disconnect between researchers, policymakers and technical experts. Bridging this gap requires intentional knowledge translation strategies that facilitate interdisciplinary collaboration and real-world application of research findings. Hackathons, which bring together diverse stakeholders in a time-bound, solution-oriented format, offer an approach to address this challenge. In January 2025, the MRC Centre for Global Infectious Disease Analysis and the Centre for Epidemiological Modelling and Analysis at the University of Nairobi organised the Bridging the Gap Hackathon, designed to strengthen collaboration between academia, policy and public health practitioners in Kenya. The hackathon convened researchers, software engineers and policymakers to co-develop data-driven tools to tackle public health challenges identified by Kenya's Ministry of Health and the Directorate of Veterinary Services. Over five days and using a structured multi-stage process, six interdisciplinary teams developed prototype solutions to improve outbreak surveillance, vaccine deployment, data quality monitoring and health workforce estimation. This paper reflects on the hackathon's structure, participant experiences and project outcomes, highlighting key lessons for future knowledge translation initiatives. Our findings suggest that hackathons can serve as effective platforms for accelerating interdisciplinary research impact, fostering engagement between policymakers and researchers and promoting the development of solutions to public health issues.
Takeda G-protein-coupled receptor 5 (TGR5) and farnesoid X receptor (FXR) are bile acid-activated receptors involved in glucose, lipid, and energy homeostasis, making them promising therapeutic targets for type 2 diabetes mellitus (T2DM) and metabolic liver diseases. This review critically analyzes patents published between 2015 and 2025 retrieved from WIPO Patentscope, Espacenet, USPTO, and Google Patents using keyword- and IPC-based strategies. Major patented chemotypes include modified bile acids, benzoic acid-cholane hybrids, heteroaryl scaffolds, and sulfonylurea/sulfonamide derivatives. Several compounds demonstrated sub micromolar (µM) to nanomolar (nM) TGR5/FXR agonistic activity, while gut-restricted agonists showed enhanced GLP-1 secretion with reduced systemic adverse effects such as gallbladder filling and pruritus. Comparative patent analysis revealed a progressive transition from classical steroidal scaffolds toward tissue-selective and gut-restricted modulators designed to improve receptor selectivity, pharmacokinetics, and translational safety. Despite strong preclinical promise, the clinical translation of TGR5 and FXR agonists remains limited by mechanism-driven toxicities and inadequate long-term tolerability. Future progress will likely depend on tissue-selective, pathway-biased, and gut-restricted modulation rather than further increases in receptor potency.
Activity-dependent synaptic plasticity is governed by posttranslational mechanisms that regulate the stability and molecular organization of postsynaptic protein complexes. Proline-directed phosphorylation of the N-terminus of PSD-95 promotes synaptic weakening during NMDAR-dependent LTD, yet this type of phosphorylation also alters the cis-trans isomerization of the adjacent peptidyl-prolyl bond. Despite its predicted importance, these conformational changes have not been directly measurable using existing molecular tools. Here we describe the development of novel conformation-preferential antibodies that distinguish structural states of PSD-95 when Threonine 19 (T19), a site implicated in NMDAR-LTD, is phosphorylated. These antibodies were validated biochemically and in cellular assays, where signal increased following GSK3β-mediated phosphorylation and was lost upon dephosphorylation. These reagents represent the first conformation-preferential antibody-based tools capable of reporting phosphorylation-dependent conformational states of PSD-95 at T19. This strategy validates and expands prior framework for developing conformational-sensitive antibodies, an approach that can be applied to other synaptic proteins.Significance Statement Post-translational modifications regulate synaptic proteins not only through changes in chemical composition but also by altering protein conformation. Phosphorylation of PSD-95 at Threonine 19 is required for NMDA receptor-dependent synaptic weakening, yet the associated cis-trans isomerization of the adjacent proline bond has been inaccessible to experimental analysis. In this study, we introduce the first conformation-preferential antibodies capable of distinguishing phosphorylation-dependent structural states of PSD-95 at this site. These reagents provide a new molecular toolkit for investigating how proline-directed phosphorylation and isomerization regulate synaptic scaffolds during plasticity and disease.
Neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis, are defined by the progressive loss of neurons through interconnected pathological mechanisms, including oxidative stress, mitochondrial dysfunction, protein aggregation, and neuroinflammation. Accumulating evidence implicates metal dyshomeostasis as a central and multifaceted contributor to these mechanisms, with roles ranging from a primary pathogenic driver in AD and PD, to a secondary amplifier of genetic pathology in HD and ALS, and as a contextual risk modifier in the presence of toxic metals. Essential trace metals such as iron, zinc, copper, manganese, selenium, iodine, and molybdenum are vital for neurotransmission, antioxidant defense, and cellular metabolism. Dysregulation of these metals disrupts redox balance, impairs proteostasis, and activates regulated cell death pathways, including ferroptosis and cuproptosis. Toxic metals, such as lead, cadmium, and mercury, exacerbate neurodegeneration by displacing essential metals, inducing oxidative injury, and promoting protein misfolding and neuroinflammation. This narrative review synthesizes mechanistic, experimental, genetic epidemiological, and clinical evidence to critically evaluate the contributions of both essential and toxic metals to neurodegeneration in AD, PD, HD, and ALS. We examine the genetic, environmental, and physiological determinants of metal homeostasis; the analytical techniques for quantifying metals in clinical samples; and clinical trial data on metal-targeted therapeutic strategies. Notably, iron chelation with deferiprone consistently reduces brain iron on neuroimaging but worsens clinical outcomes in both PD and AD, presenting a translational paradox that requires mechanistic re-evaluation. We also provide methodological recommendations for interpreting Mendelian randomization studies of metal exposures and propose translational priorities to advance metal-targeted diagnostics and therapeutics for neurodegenerative diseases.
Lung cancer is the malignant tumor with the highest incidence and mortality rates in China. Driver gene-negative non-small cell lung cancer (NSCLC), as a common subtype of lung cancer, imposes a substantial disease burden. With the continuous enrichment of treatment options, these patients have many treatment choices, but the translation of efficacy data from clinical research to real-world clinical practice still faces many challenges. Furthermore, as a highly heterogeneous disease, lung cancer is full of complexity in clinical diagnosis and prognosis assessment, and there are numerous controversies in diagnostic and therapeutic strategies. Multi-disciplinary team (MDT) management can fully consider individual differences and tumor heterogeneity, bringing comprehensive clinical benefits to patients with driver gene-negative NSCLC from diagnosis to treatment, but there are still many unclear aspects regarding its application scenarios. Therefore, the Oncology Multidisciplinary Medical Committee of the Chinese Medical Doctor Association, the Lung Cancer Group of the Oncology Branch of the Chinese Medical Doctor Association, the Expert Committee on Quality Control of Lung Cancer of the National Cancer Quality Control Center, and the Non-Small Cell Lung Cancer Committee of the Chinese Society of Clinical Oncology jointly initiated and organized a multidisciplinary panel of experts to conduct an in-depth discussion on the application scenarios of MDT in the clinical practice of driver gene-negative NSCLC. Based on the discussions and voting results of the expert panel, a final expert consensus on the implementation of MDT in different clinical scenarios was developed, aiming to provide Chinese clinicians with practical guidance for MDT practice. 肺癌是中国发病率和死亡率最高的恶性肿瘤，驱动基因阴性非小细胞肺癌（NSCLC）作为肺癌中的常见亚型，疾病负担沉重。随着治疗手段的不断丰富，该类患者拥有诸多治疗选择，但临床研究中的疗效数据向真实世界临床实践转化仍面临诸多挑战。此外，肺癌作为一种高度异质性疾病，在临床诊断和预后评估中充满复杂性，诊疗策略存在诸多争议。多学科诊疗（MDT）可充分考虑个体差异和肿瘤异质性，从诊断到治疗为驱动基因阴性NSCLC患者带来全方位的临床获益，但其应用场景仍存在诸多不明确之处。因此，中国医师协会肿瘤多学科诊疗专业委员、中国医师协会肿瘤医师分会肺癌学组、国家肿瘤质控中心肺癌质控专家委员会、中国临床肿瘤学会非小细胞肺癌专家委员会共同发起并组织多学科专家，就驱动基因阴性NSCLC临床实践中MDT的应用场景展开深入探讨。基于专家组的讨论及投票结果，最终形成了针对不同临床情境下MDT实施的专家共识，旨在为中国临床医师提供切实可行的MDT实践指导。.
To provide a comprehensive review of the biological rationale, clinical evidence, and practical perioperative management of immunotherapy for the head and neck surgeon. Standard treatment for resectable head and neck squamous cell carcinoma (HNSCC) has reached a survival plateau, with over 50% of patients experiencing recurrence. The integration of immune checkpoint inhibitors (ICIs) into the neoadjuvant window represents a paradigm shift toward biologically adapted surgical intervention. Neoadjuvant immunotherapy capitalizes on an intact immune substrate to create an in situ vaccine, avoiding the post-surgical immune desert that limits adjuvant efficacy. Emerging phase III data confirm that perioperative ICI significantly improves event-free survival. Successful implementation requires the surgeon to navigate unique diagnostic challenges, such as distinguishing rare but anatomically risky pseudoprogression from true progression. While combination therapies (chemoimmunotherapy or immunoradiotherapy) yield higher pathologic complete response rates, they also increase toxicity. Intraoperatively, ICI monotherapy generally preserves tissue planes without increasing surgical delays or major wound complications. Standard biomarkers like PD-L1 and TMB, alongside emerging tools such as liquid biopsy (ctDNA), are essential for patient selection and dynamic monitoring. The transition to neoadjuvant immunotherapy facilitates future surgical de-escalation and function-preserving approaches. To optimize outcomes, the modern surgeon must act as a surgical immunologist, interpreting translational data to guide real-time operative planning.
Cancer cells frequently reside in a glucose-deprived microenvironment due to rapid tumor proliferation and insufficient angiogenesis. However, the mechanisms by which colorectal cancer cells (CRC) adapt to glucose starvation to sustain proliferation remain unclear. Succinylation, a novel post-translational modification, has been implicated in regulating tumor cell proliferation and survival under nutrient stress. Our study reveals that fumarate hydratase (FH), a key enzyme in the tricarboxylic acid (TCA) cycle, is downregulated in CRC and acts as a tumor suppressor. Under glucose starvation mimicked in vitro, FH protein expression is reduced, leading to abnormal accumulation of its upstream metabolites fumarate and succinate, which correlates with advanced clinical stage and poor prognosis in CRC patients. Mechanistically, accumulated fumarate specifically binds to and stabilizes the NRF2 protein, upregulating the expression of GPX4 and FTH1 to inhibit ferroptosis, thereby sustaining CRC cell proliferation. Meanwhile, glucose starvation induces CPT1A-mediated succinylation of FH at residues K66/K80, reducing FH protein stability and promoting its degradation via the autophagy-lysosome pathway. Our findings reveal the critical role of FH and its succinylation in CRC cell adaptation to glucose starvation, inhibiting ferroptosis, and maintaining cancer cell proliferation, providing novel potential targets and a theoretical basis for the clinical treatment of CRC.
Misophonia is an emerging condition in which everyday sounds, such as chewing, sniffing, or tapping, evoke disproportionately intense emotional and physiological responses. Despite growing recognition of its clinical significance, progress in understanding misophonia has been hindered by the limited availability of standardized and ecologically valid stimulus sets. Here, we present a large, open-access archive of over 1,400 five-second audiovisual clips spanning 12 empirically informed categories of misophonic triggers. This resource includes a diverse array of real-world triggers and extends beyond commonly studied orofacial movement-related sounds, while its audiovisual format enables systematic investigation of how visual context shapes responses to misophonic sounds. The archive lowers the barrier for laboratories to study misophonia, promotes reproducibility across sites, and may support applications ranging from crowdsourced assessments of population-level sensitivities to machine learning approaches for automated trigger detection. By providing a large and diverse audiovisual misophonia stimulus repository, this resource is designed to accelerate mechanistic, clinical, and translational research on misophonia and related sensory-emotional phenomena.
Glioblastoma (GB) is a WHO grade 4 brain cancer with dismal prognosis, yet its aetiology remains poorly defined. Although viral involvement has been proposed, findings across studies remain inconsistent, reflecting inherent limitations of individual technologies and cohort size. Here we applied metaproteomic profiling to a publicly available GB proteome dataset (12 control, 21 adjacent, 159 tumour) and an independent cohort of 81 samples (37 control, 44 tumour) to detect viral proteins in tumour and controls tissues. Across cohorts, we detected viral proteins from diverse species, with human herpesviruses (HHV-1, 2, and 8) more frequently detected in GB tumours compared with control tissues. Analysis of the host tumour proteome revealed differential abundance of proteins related to transcriptional regulation, RNA processing, protein translation, immune responses, and mitochondrial-associated metabolism. Correlation analysis identified associations between viral and human proteins, with several linked to biological processes previously implicated in DNA virus-host interactions. Further stratification of tumour by HHV-1 status showed consistent alterations in proteins associated with mitochondrial-associated metabolism, protein turnover, and cell adhesion/signalling.In summary, this study demonstrates the feasibility of metaproteomics for detecting viral components in archival GB tissues. Using this approach, we observed differences in viral protein landscape across cohorts and identified associations between viral presence and host proteomic features, providing a protein-level framework for future studies of virus-host interactions in GB.
Emergency EEG (emEEG) is increasingly used in the emergency department (ED), but its diagnostic yield remains uncertain. This protocol describes a multicentre observational study aiming to evaluate emEEG findings and their relationship with diagnostic pathways and therapeutic management of patients admitted to the ED. This multicentre retrospective study will analyse emEEGs performed on patients admitted to the ED of some Italian teaching and community hospitals over a 1-year period with a target sample size of 3850 patients. The diagnostic yield of emEEG will be evaluated by assessing abnormal and epileptiform findings and the relationship between emEEG findings and subsequent clinical decisions, including confirmation or revision of the initial diagnostic suspicion, decisions regarding home discharge or hospitalisation and medication changes. EEG will be classified according to the terminology of the American Clinical Neurophysiology Society. Clinical and instrumental data will be respectively reviewed by emergency physicians and neurologists/neurophysiologists. In particular, via traditional biostatistics and interpretable machine learning models, the study will evaluate the diagnostic yield of emEEG and its association with subsequent clinical management across defined clinical scenarios in the ED. This first large-scale multicentre protocol will provide valuable insights for emergency department (ED) clinicians in selecting appropriate candidates for an emergency EEG (emEEG), supporting ethically sound, proportionate use of this resource in a time- and risk-critical setting. By clarifying diagnostic yield and its relationship with subsequent clinical decisions, the study is expected to generate robust evidence to guide emEEG ordering, reduce unnecessary testing and delays, and promote safer, more equitable decision-making (including appropriate home discharge) while minimising potential harms from misdiagnosis or overtreatment. The study has been approved by the Ethics Committee Regione Toscana - Area Vasta Centro (n. 27241). Findings will be disseminated through peer-reviewed publications, conference presentations and engagement with relevant clinical societies to inform international recommendations and facilitate translation into ED practice. Furthermore, developed models will be made openly available for external and public validation.
Accurate and timely assessment of consciousness is critical for triage, escalation of care, and patient safety in emergency and hospital settings. However, documentation using the AVPU scale (Alert, Verbal, Pain, Unresponsive) remains inconsistent owing to high workload, subjectivity, and fragmented workflows. This study developed and evaluated Consc.ia, a video-based clinical decision-support platform that automates AVPU inference while preserving clinician oversight and enabling seamless, interoperable documentation through HL7 FHIR. A simulated AVPU dataset comprising 136 videos from 58 healthcare professionals (physicians, nurses, paramedics, and first responders) was created under controlled conditions with ethics approval from the ISCTE - Instituto Universitário de Lisboa Ethics Commission (reference CE-ISTA/2025.08, July 2025). The system architecture combines edge-computing computer vision for real-time extraction of facial landmarks, eye state, arm movement, and verbal responses; a clinician-in-the-loop validation layer; and FHIR-mapped Observation resources for direct EHR integration. Three deployment scenarios (Emergency Medical Services, Emergency Departments, and Intermediate Care wards) were designed and compared. Technology adoption was modelled using Rogers' Innovation Adoption Curve and the Bass Diffusion Model (p = 0.01, q = 0.35, M = 111 Portuguese hospitals). The architecture achieves low-latency inference with privacy-by-design (local processing, no raw video storage). Stakeholder validation confirmed strong workflow fit and highlighted persistent documentation gaps during EMS-to-hospital transitions. Scenario analysis revealed distinct hardware and integration requirements (ambulance edge device versus ward multi-camera server). Bass modelling projects gradual adoption, reaching approximately 50% of Intermediate Care wards by 2037 in the realistic scenario, with the "chasm" phase occurring between 2030 and 2032. Sensitivity analysis identified early clinical evidence and FHIR integration support as the strongest accelerators of diffusion. As this constitutes a proof-of-concept study, no quantitative AVPU classification metrics (e.g., accuracy, sensitivity, specificity, or confusion matrix) are reported at this stage; empirical model evaluation against expert-annotated clinical recordings is identified as the primary prerequisite for future validation and clinical translation. As a proof-of-concept that has not yet undergone clinical validation, Consc.ia offers a feasible, interoperable solution for standardising AVPU documentation and strengthening early warning systems. By combining video analytics, edge computing, clinician validation, and FHIR integration, the platform addresses a longstanding gap in emergency-care digitalisation and provides a clear roadmap for real-world adoption.
Computational medicine uses mathematical modelling, high-performance computing, and the availability of large-scale biomedical data to study multiscale complex diseases. This review synthesises recent developments in in silico oncology, neurology, and epidemiology, highlighting their shared methodological foundations in systems theory and complex networks. We discuss how combining mechanistic approaches with machine learning integrates heterogeneous data. Finally, we identify translation barriers and outline future directions like digital twins for predictive, personalised healthcare.
Engineering the genetic code-by reassigning multiple of the 64 natural codons-enables making organisms resistant to all viruses, preventing genetic information exchange, and allowing the biosynthesis of genetically encoded unnatural polymers. However, synonymous codon replacement-recoding-is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using genome synthesis, directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple synthetic Escherichia coli genomes. We construct six partially recoded E. coli strains bearing up to 45.8% of a synthetic genome with a deleterious 57-codon genetic code. As our analyses revealed widespread defects-including unassigned codons in Syn61 and Syn57-we apply multi-omics to revise our genome design and mitigate defects. Using multi-omics, we show that recoding induces transcriptional and translational changes leading to fitness defects under hundreds of conditions. Finally, we develop a multi-omics-guided evolution strategy that rapidly restores fitness, enabling genome synthesis with radical changes.
Vitiligo is a chronic autoimmune depigmenting disorder affecting 0.5%-2% of the global population, characterized by bidirectional interplay between psychological stress and disease progression, with accumulating evidence highlighting the central role and translational relevance of the neuro-endocrine-immune-cutaneous axis in its pathogenesis. Epidemiological data indicate over half of patients experience significant psychological stress prior to disease onset, while visible depigmentation markedly elevates the burden of depression and anxiety, establishing a self-amplifying pathogenic loop. Mechanistically, neural crest-derived melanocytes form functional "neuro-pigment units" with intraepidermal nerve endings, enabling bidirectional communication via neuropeptides including calcitonin gene-related peptide (CGRP) and substance P. Dynamic crosstalk among keratinocytes, sensory neurons, and melanocytes integrates neurotrophic and inflammatory signals to tightly regulate melanocyte survival and biological function. Sympathetic activation drives melanocyte injury via norepinephrine-mediated β2-adrenergic receptor signaling, while dopamine metabolites exacerbate apoptosis via the oxidative stress-Akt-Bad axis; context-dependent hypothalamic-pituitary-adrenal axis effects and light-melatonin-circadian clock disruption further promote immune dysregulation and melanocyte loss. Notably, neuromodulatory approaches like transcutaneous auricular vagus nerve stimulation show therapeutic promise by attenuating oxidative stress and limiting pathogenic CD8⁺ T-cell infiltration. These insights have fostered targeted strategies including CGRP receptor antagonists and dual antioxidant-neuroprotective natural compounds. Integrating neuroimmunological modulation with psychological and circadian interventions represents a promising precision medicine framework for vitiligo management.
The earliest stages of Alzheimer's disease (AD) are frequently characterized by neuropsychiatric symptoms (NPS) such as anxiety, agitation, depression, compulsivity, appetite dysregulation, and sleep disturbances, often preceding measurable cognitive decline. Evidence from clinical and animal studies implicates hyperactivity of the locus coeruleus-norepinephrine (LC-NE) system as a mechanistic driver of these behaviors. Here, we review noradrenergic circuits that can potentially underlie psychiatric disturbances to identify therapeutic targets for preventing and delaying onset of AD. Given that this system influences attention, arousal, mood, and stress responses, LC-NE hyperactivity across circuitry involving amygdala, thalamus, hypothalamus, anterior cingulate cortex, prefrontal cortex, and olfactory areas can contribute to NPS features in early AD. Advances in neuroimaging and physiological measures of noradrenergic function have enabled in vivo tracking of LC integrity and NE transmission, offering the opportunity to detect LC-NE dysfunction early in disease progression and potentially implement targeted pharmacologic and neuromodulatory interventions to restore optimal LC-NE tone. Overall, dissection of LC-NE circuitry and its clinical translation hold promise for developing biomarker-driven, stage-specific interventions to reduce NPS burden and enhance the efficacy of disease-modifying therapies in AD.
Sexual hormone receptors (SHRs) are essential for breast cancer (BCa) pathogenesis. BCa is a prevalent malignancy with high heterogeneity and high recurrence. Endocrine resistance remains a major clinical challenge. SHR-mediated transcription involves complex epigenetic, post-translational, and inter-receptor crosstalk, and dysregulation of these processes contributes to endocrine resistance. This review aims to summarize the current progress on the molecular mechanism underlying the function of nucleic SHRs in BCa, providing insights for the novel therapeutic strategies in BCa.
Ovarian cancer is a gynecological malignancy associated with high mortality and poses significant clinical challenges in early diagnosis and precision treatment. Although the rapid advancement of artificial intelligence (AI) has introduced novel approaches to this field, a comprehensive bibliometric overview remains lacking. This study aims to fill this gap by providing a systematic bibliometric analysis of this rapidly evolving domain. In this study, the Web of Science Core Collection (WoSCC) was used to retrieve literature on AI applications in ovarian cancer research published from 2006 to the search date (November 19, 2025). Using CiteSpace and VOSviewer, we conducted visual and quantitative analyses of publication trends, countries/regions, institutions, authors, journals, highly cited papers, and keywords. A total of 786 publications were included in the analysis. The annual publication output showed pronounced exponential growth, with a marked acceleration after 2019. China, the United States, and the United Kingdom were the leading contributing countries. Research hotspots centered on AI-assisted diagnosis, prognostic prediction models, radiomics, and biomarker discovery. The evolution of keywords indicated that frontier research has shifted from basic classification toward more advanced areas, including high-grade serous ovarian carcinoma, multimodal learning, and explainable AI. Research on AI in ovarian cancer has progressed rapidly, with international collaboration concentrated among leading contributors such as China, the USA, and the UK. Future efforts should prioritize the development of explainable and robust clinical AI systems, deeper integration of multimodal data, closer collaboration between clinicians and AI researchers, and high-quality data sharing to facilitate the translation of research findings into precise clinical practice.
Vascular diseases, particularly atherosclerosis, represent a leading cause of global morbidity and mortality. Endovascular stenting has emerged as a cornerstone of therapy to restore vessel patency, yet conventional stents remain obstructed by significant clinical limitations, including in-stent restenosis, thrombosis, and mechanical failure. These adverse outcomes are intrinsically linked to their fundamental structural design, which is characterized by a positive Poisson's ratio, leading to foreshortening and a biomechanical mismatch with the native vasculature. This review critically examines auxetic stents as a next-generation solution, engineered with a structure possessing a negative Poisson's ratio. This unique property allows them to expand axially upon radial deployment, thereby eliminating foreshortening, enhancing conformability to tortuous vessels, and distributing mechanical stress more uniformly onto the arterial wall. This paper synthesizes the robust body of in-silico/bench-top evidence from computational modeling and in-vitro experimentation that validates these superior biomechanical characteristics. Furthermore, it explores the profound and favorable biological implications, arguing that the optimized mechanical environment and improved hemodynamics are hypothesized to attenuate the primary triggers for neointimal hyperplasia and foster rapid, complete endothelialization. The review concludes by outlining the translational pathway, including challenges in structure integration and discussing the vast future horizons for auxetic structured stents in complex peripheral, carotid, and non-vascular applications. Auxetic design represents a paradigm shift from material-centric iteration to structure-driven innovation, holding the promise to significantly improve the long-term safety and efficacy of endovascular stent implants.