Search Results (6,737)

Factor XII is a molecule of unclear physiological function that has attracted increasing research interest across multiple medical disciplines. In recent years, a substantial body of evidence has emerged regarding the contribution of factor XII to the pathogenesis of inflammatory and prothrombotic conditions. FXII has been shown to play a protective role in FXII-driven coagulation during host defence against infections and to protect against multi-organ failure in animal models of sepsis. In acute respiratory distress syndrome (ARDS), FXII activity contributes to the release of pro-inflammatory mediators and is associated with severe clinical outcomes; it also induces fibroblast migration in idiopathic pulmonary fibrosis. FXII deficiency has been associated with reduced neutrophil adhesion and migration in sterile skin wounds and immune complex-induced vasculitis. In neurological conditions, FXII deficiency significantly reduced the number and severity of multiple sclerosis relapses and decreased the volume of post-traumatic brain oedema. In heart failure pathogenesis, FXII deficiency and pharmacological inhibition of FXII activity blocked activation of the renin–angiotensin–aldosterone system (RAAS) in dilated cardiomyopathy, increased median survival, and delayed heart failure onset in murine models. Importantly, FXII inhibition prevented arterial thrombosis without affecting haemostasis. This review summarises the latest findings on the contribution of FXII to inflammatory and prothrombotic states across multiple medical fields, including cardiology. Pharmacological inhibition of FXII has generated considerable interest as a potential future therapeutic strategy; however, to date, human studies remain limited. Full article

Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors. Full article

Drug resistance is an important challenge in medical research and clinical practice, posing a serious threat to the effectiveness of current therapeutic strategies. Transcriptomics has played a crucial role in analyzing resistance-related genes and pathways, while the application of machine learning in high-throughput data analysis and prediction has also opened up new avenues in this field. However, existing studies mostly focus on a single drug or specific categories, and their conclusions are limited in applicability across drug categories, while studies on drugs beyond antibacterial and antitumor categories remain limited. In this study, we systematically analyzed the transcriptomic data of resistant cell lines treated with 1738 drugs spanning 82 categories and identified core genes through an integrated analysis of three classical machine learning methods. Using the antibacterial drug salinomycin as an example, we established a resistance prediction model that demonstrated high predictive accuracy, indicating the significant value of the selected core genes in prediction. Meanwhile, some of the core genes identified through the protein–protein interaction (PPI) network overlapped with those derived from machine learning analysis, further supporting the reliability of these core genes. Pathway enrichment analysis of differential genes revealed potential resistance mechanisms. This study provides a new perspective for exploring resistance mechanisms across drug categories and highlights potential directions for resistance intervention strategies and novel drug development. Full article

Vascular diseases remain a major global health burden despite remarkable technological advances in vascular surgery and endovascular therapies. Conditions such as peripheral arterial disease, abdominal aortic aneurysm, carotid stenosis, chronic venous disease, diabetic vasculopathies, and vascular chronic ulcers are not only biological entities but are deeply shaped by social structures, cultural norms, and economic inequalities. This article introduces Vascular Sociology as an interdisciplinary field that integrates vascular surgery with medical sociology to provide a more comprehensive understanding of vascular health and disease. Drawing on classical and contemporary sociological theory, including concepts such as social determinants of health, embodiment, illness narratives, and the disease–illness–sickness triad, the article argues that vascular pathology reflects cumulative social exposures across the life course. Socially patterned behaviors, work conditions, food environments, healthcare access, gender norms, and geographic inequalities profoundly influence disease onset, progression, treatment decisions, and outcomes. The paper highlights how surgical success is contingent not only on technical excellence but also on patients’ social contexts, including health literacy, trust in institutions, caregiving resources, and the capacity to adhere to long-term follow-up and rehabilitation. By outlining conceptual foundations, epidemiological evidence, and mixed-methods research strategies, the article positions Vascular Sociology as a framework capable of bridging biomedical knowledge with lived experience. This approach expands the definition of vascular outcomes to include social reintegration, identity transformation, and equity of care, ultimately aiming to improve patient-centered practice, reduce disparities, and inform more socially responsive vascular health policies. Full article

Contemporary medical practitioners increasingly recognize the critical impact of healing-environment design on patients’ recovery, positioning it as a pivotal consideration in healthcare facility planning. While existing research has predominantly focused on enhancing the functionality and efficiency of healthcare environments, it has often overlooked the significance of individual patient needs and their distinct experiences. This paper aims to utilize the principles of epidemiology and empirical analysis to explore the application and research trends of the patient-centered care (PCC) concept in healthcare facility design, to promote interdisciplinary collaboration and achieve customized healthcare environments. Based on bibliometric analysis and key literature review methods, this paper systematically examines and interprets the research development trends of PCC in healing environment design, integrating both macro and micro perspectives, and reveals how design factors in therapeutic environments support the realization of PCC principles, thereby improving patients’ rehabilitation experiences and health outcomes. The results indicate that current research on PCC is trending towards increasingly diversified integration via high-frequency keywords such as recovery, healing environment, and evidence-based design, highlighting the shift from functional optimization to emotional care, technological integration, and nature-based interactions in design. Notably, patient-centered care has become a consensus and core integrating concept in this field. This paper not only reveals the key role of healing environments in constructing PCC practice pathways but also provides theoretical support and strategic reference for the planning of healthcare spaces and the collaborative design of nursing processes, and demonstrates that healing environments have evolved from passive spaces into active rehabilitation mediums through interdisciplinary collaboration, thereby facilitating the implementation of the patient-centered healthcare philosophy. Full article

FLASH radiotherapy requires real-time, non-invasive beam monitoring systems capable of operating under ultra-high dose rate (UHDR) conditions without perturbing the therapeutic beam. In this work, we characterized the extraction system of Supersonic Gas Curtain-based Ionization Profile Monitor (SGC-IPM) for its capabilities as a transverse beam profile and position monitor for FLASH protons. The monitor utilizes a tilted gas curtain intersected by the incident beam, leading to the generation of ions that are extracted through a tailored electrostatic field, and detected using a two stage microchannel plate (MCP) coupled to a phosphor screen and CMOS camera. CST Studio Suite was employed to conduct electrostatic and particle tracking simulations evaluating the ability of the extraction system to measure both beam profile and position. The ion interface, at the interaction region of proton beam and gas curtain, was modeled with realistic proton beam parameters and uniform gas curtain density distributions. The ion trajectory was tracked to evaluate the performance across multiple beam sizes. The simulations suggest that the extraction system can reconstruct transverse beam profiles for different proton beam sizes. Simulations also supported the system’s capability as a beam position monitor within the boundary defined by the beam size, the dimensions of the extraction system, and the height of the gas curtain. Some simulation results were benchmarked against experimental data of 28 MeV proton beam with 70 nA average beam current. This study will further help to optimize the design of the extraction system to facilitate the integration of SGC-IPM in medical accelerators. Full article

Background: Pulmonary infarction (PI) is the result of the occlusion of distal pulmonary arteries resulting in damage to downstream lung areas that become ischemic, hemorrhagic, or necrotic, and it is often a complication of an underlying condition such as pulmonary embolism (PE). Since in most of cases it is located peripherally, lung ultrasound (LUS) can be a good evaluation tool. The typical radiological features of PI are well-known; however, there are limited data on its sonographic characteristics and its evolution. Methods: The aim of this study is to evaluate, using LUS, a convenience sample of patients with acute PE with computed tomography (CT) consolidation findings consistent with PI. Patients’ clinical characteristics were collected and LUS findings at baseline and their short-term progression was assessed. LUS was performed within 72 h of PE diagnosis (T0) and repeated after one (T1) and four weeks (T2). Each procedure started with a focused examination of the areas of lesions based on CT findings, followed by an exploration of the other posterior and lateral lung fields. The convex probe was used for initial evaluation integrating LUS evaluation with the linear one was employed for smaller and more superficial lesions and when appropriate. Color Doppler mode was added to study vascularization. Results: From June to October 2023, 14 consecutive patients were enrolled at the Respiratory Unit of the University Hospital of Pisa. The main population characteristics included the absence of respiratory failure and prognostic high-risk PE (100%), the absence of significant comorbidities (79%), and the presence of typical symptoms, such as chest pain (57%) and dyspnea (50%). The average number of consolidations per patient was 1.4 ± 0.6. Follow-up LUS showed the disappearance of some consolidations and some morphological changes in the remaining lesions: the presence of hypoechoic consolidation with a central hyperechoic area (“bubbly consolidation”) was more typical at T1 while the presence of a small pleural effusion often persisted both at T1 and T2. A decrease in wedge/triangular-shaped consolidations was observed (82% at T0, 67% at T1, 24% at T2), as was an increase in elongated shapes, representing a residual pleural thickening over time (9% at T0, 13% at T1, 44% at T2). A reduction in size was also observed by comparing the mean diameter, long axis, and short axis measurements of each consolidation at the three different studied time points: the average of the short axes and the median of the mean diameters showed a statistically significant reduction after four weeks. Additionally, a correlation between lesion size and pleuritic pain was described, although it did not achieve statistical significance. Conclusions: Patients’ clinical characteristics and ultrasound features are consistent with previous studies studying PI at PE diagnosis. Most consolidations detected by LUS change over time regarding size and form, but a minority of them do not differ. LUS is a safe and non-invasive exam that could help to improve patients’ clinical approach in emergency rooms as well as medical and pulmonology settings, clinically contextualized for cases of chest pain and dyspnea. Future studies could expand the morphological study of PI. Full article

Over recent years, venous ulcer wound care has experienced significant advancements through the application of machine learning (ML) models. The aim of the present study is a systematic, comprehensive analysis of prior research studies in this field covering the period between 2001 and August 2025. By searching multiple academic databases, including the Web of Science, Scopus, and PubMed, using relevant keywords and different queries, and screening reference lists of previously published manuscripts and review papers with a focus on the application of artificial intelligence in dermatology and medicine, an initial set of potential studies for review was obtained. To ensure the scope and relevance of the review, several inclusion and exclusion criteria were used to derive the final set of relevant research studies upon which a database for research data management was created. As a result, a total of 79 relevant research studies were comprehensively analysed, upon which detailed meta-analysis and analysis of application areas of ML models within venous ulcer wound care were conducted. Afterwards, a summary of benefits for medical systems and patients was given along with a general discussion regarding ML model limitations, trends, and opportunities, as well as research studies’ limitations and possible future research directions. The presented analyses may be valuable for researchers interested in applying ML models not only to venous ulcer wound care but also to other types of chronic wound care. Full article

Microwave sensing technology is rapidly advancing and increasingly finding its way into biomedical applications, promising significant improvements for medical care. Concurrently, the rise of artificial intelligence (AI) is enabling significant enhancements in the biomedical domain. Close scrutiny of the recent literature reveals intense activity in both fields, with particularly impactful outcomes deriving from the combined use of advanced microwave techniques and AI for biomedical monitoring. In this review, an up-to-date compilation, from the perspective of the authors, of the most significant works published on these topics in recent years is given, focusing on their integration and current challenges. With the objective of analyzing the current landscape, we survey and compare state-of-the-art biosensors and imaging systems at all healthcare levels, from outpatient contexts to specialized medical equipment and laboratory analysis tools. We also delve into the relevant applications of AI in medicine for processing microwave-derived data. As our core focus, we analyze the synergistic integration of AI in the design of microwave devices and the processing of the acquired data, which have shown notable performances, opening new avenues for compact, affordable, and multi-functional medical devices. We conclude by synthesizing the prevailing technical, algorithmic, and translational challenges that must be addressed to realize this potential. Full article

The lack of clinical data for chronic kidney disease (CKD) prediction frequently results in model overfitting and inadequate generalization to novel samples. This research mitigates this constraint by utilizing a Conditional Tabular Generative Adversarial Network (CTGAN) to enhance a constrained CKD dataset sourced from the University of California, Irvine (UCI) Machine Learning Repository. The CTGAN model was trained to produce realistic synthetic samples that preserve the statistical and feature distributions of the original dataset. Multiple machine learning models, such as AdaBoost, Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were assessed on both the original and enhanced datasets with incrementally increasing degrees of synthetic data dilution. AdaBoost attained 100% accuracy on the original dataset, signifying considerable overfitting; however, the model exhibited enhanced generalization and stability with the CTGAN-augmented data. The occurrence of 100% test accuracy in several models should not be interpreted as realistic clinical performance. Instead, it reflects the limited size, clean structure, and highly separable feature distributions of the UCI CKD dataset. Similar behavior has been reported in multiple previous studies using this dataset. Such perfect accuracy is a strong indication of overfitting and limited generalizability, rather than feature or label leakage. This observation directly motivates the need for controlled data augmentation to introduce variability and improve model robustness. The dataset with the greatest dilution, comprising 2000 synthetic cases, attained a test accuracy of 95.27% utilizing a stochastic gradient boosting approach. Ensemble learning techniques, particularly gradient boosting and random forest, regularly surpassed conventional models like KNN in terms of predicted accuracy and resilience. The results demonstrate that CTGAN-based data augmentation introduces critical variability, diminishes model bias, and serves as an effective regularization technique. This method provides a viable alternative for reducing overfitting and improving predictive modeling accuracy in data-deficient medical fields, such as chronic kidney disease diagnosis. Full article

Additive manufacturing has been adopted in several industries including the medical field to develop new personalised medical implants including tissue engineering scaffolds. Custom patient-specific scaffolds can be additively manufactured to speed up the wound healing process. The aim of this study was to design, fabricate, and evaluate a range of materials and scaffold architectures for 3D-printed wound dressings intended for soft tissue applications, such as skin repair. Multiple biocompatible polymers, including polylactic acid (PLA), polyvinyl alcohol (PVA), butenediol vinyl alcohol copolymer (BVOH), and polycaprolactone (PCL), were fabricated using a material extrusion additive manufacturing technique. Eight scaffolds, five with circular designs (knee meniscus angled (KMA), knee meniscus stacked (KMS), circle dense centre (CDC), circle dense edge (CDE), and circle no gradient (CNG)), and three square scaffolds (square dense centre (SDC), square dense edge (SDE), and square no gradient (SNG), with varying pore widths and gradient distributions) were designed using an open-source custom toolpath generator to enable precise control over scaffold architecture. An in vitro degradation study in phosphate-buffered saline demonstrated that PLA exhibited the greatest material stability, indicating minimal degradation under the tested conditions. In comparison, PVA showed improved performance relative to BVOH, as it was capable of absorbing a greater volume of exudate fluid and remained structurally intact for a longer duration, requiring up to 60 min to fully dissolve. Tensile testing of PLA scaffolds further revealed that designs with increased porosity towards the centre exhibited superior mechanical performance. The strongest scaffold design exhibited a Young’s modulus of 1060.67 ± 16.22 MPa and withstood a maximum tensile stress of 21.89 ± 0.81 MPa before fracture, while maintaining a porosity of approximately 52.37%. This demonstrates a favourable balance between mechanical strength and porosity that mimics key properties of engineered tissues such as the meniscus. Overall, these findings highlight the potential of 3D-printed, patient-specific scaffolds to enhance the effectiveness and customisation of tissue engineering treatments, such as meniscus repair, offering a promising approach for next-generation regenerative applications. Full article

Non-homologous end joining (NHEJ) is a critical DNA double-strand break (DSB) repair pathway that operates throughout the cell cycle to maintain the genomic stability of the cell. Unlike homologous recombination (HR), NHEJ is capable of repairing DSBs without the need for a homologous template, making it a rapid response mechanism, but potentially prone to errors. Central to NHEJ function and essential for the ligation through the recruitment and activation of additional repair factors, such as Artemis, XRCC4, and DNA ligase IV, is the DNA-dependent protein kinase (DNA-PK) complex. Dysregulation in the NHEJ pathway contributes to genomic instability, oncogenesis, and resistance to genotoxic therapies. Consequently, inhibitors of DNA-PK have emerged as promising therapeutic agents to sensitize tumor cells to radiation and DNA-damaging chemotherapeutics. Inhibiting the DNA-PK ability to recruit the protein complex needed for successful DSB repair promotes cell death through apoptosis or mitotic catastrophe. While inhibitors of DNA-PK can be used to enhance the effects of genotoxic therapies, the field still struggles to address critical problems: how to best exploit the differential DNA repair capacities among tumor subtypes, how to maximize radiosensitization of cancerous cells while sparing normal tissues, and how to translate preclinical studies into clinical benefits. Given that NHEJ constitutes the primary line of defense against radiation-induced damage, rapidly repairing the majority of double-strand breaks throughout the cell cycle, this review concentrates on targeting the DNA-PK complex, as the master regulator of this rapid-response mechanism, highlighting why its inhibition represents a strategic action to overcome intrinsic radioresistance. The implementation of DNA-PK inhibitors into medical practice can enable the stratification of oncologic patients into two categories, based on the tumors’ vulnerability to NHEJ disruptions. Thus, the therapeutic pathways of patients with NHEJ tumors could branch, combining traditional genotoxic therapies (radiation and DNA-damaging chemotherapeutics) with DNA-PK inhibitors to achieve an enhanced effect and improved survival outcomes. Full article

Driven by societal and technological progress, the polymer tubing industry is increasingly focused on sustainable and biodegradable products, with polylactic acid (PLA)-based microtubes gaining attention for applications such as medical stents and disposable straws. However, their inherent mechanical limitations, especially under hoop loading and the brittleness of PLA, restrict broader use. Although two-dimensional nanofillers can enhance polymer properties, conventional extrusion only creates uniaxial alignment, leaving fillers randomly oriented in the radial plane and failing to improve hoop performance. To address this, we developed a rotational extrusion strategy that superimposes a rotational force onto the conventional axial flow, generating a biaxial stress field. By adjusting rotational speed to regulate hoop stress, a concentric, interlocked graphene oxide network in a PLA/polybutylene adipate terephthalate microtube is induced along the circumferential direction without disturbing its axial alignment. This architecturally tailored structure significantly enhances hoop mechanical properties, including high compressive strength of 0.54 MPa, excellent low-temperature impact toughness of 0.33 J, and improved bending resistance of 30 N, while maintaining axial mechanical strength exceeding 50 MPa. This work demonstrates a scalable and efficient processing route to fabricate high-performance composite microtubes with tunable and balanced directional properties, offering a viable strategy for industrial applications in medical, packaging, and structural fields. Full article

Frailty is a clinical syndrome originally described in geriatrics but increasingly recognized across multiple medical fields. A wide variety of clinical tools have been developed to identify and quantify frailty in different contexts. In oncology, the Performance Status (PS) has long guided therapeutic decisions; however, with the evolution of cancer treatments and the aging of the patient population, a more comprehensive assessment of frailty is emerging as a valuable clinical tool. In patients with cirrhosis, frailty may manifest earlier than in the general population, and the Liver Frailty Index (LFI) has gained prominence as a validated measure among liver transplant candidates. Individuals with hepatocellular carcinoma (HCC) may exhibit frailty due to both the underlying cirrhosis and tumor burden. Nonetheless, evidence on the role of frailty in guiding treatment decisions for HCC remains limited, and standardized assessment tools are still lacking to optimize patient stratification and therapeutic allocation. Full article

Shallot (Allium hirtifolium Boiss.) is of considerable nutritional and medical significance due to its strong antioxidant properties; however, no nanophytotoxicity studies have assessed whether the use of nanofertilizers would improve shallot performance, micronutrient iron (Fe) enrichment, and yield in semi-arid regions. Herein, we evaluated the effects of magnetite nanoparticles (nFe₃O₄) on shallot grown for a full lifecycle in two semi-arid regions through bulb-priming followed by foliar application and compared them with conventional ferrous sulfate (FeSO₄) fertilizer and untreated control. Our results showed remarkable cellular adaptations to semi-arid climate upon nFe₃O₄ treatment as leaves displayed thickened cell walls, distinct chloroplasts featuring organized thylakoid grana and stroma, normal mitochondria, abundant starch grains, and plastoglobuli around chloroplasts compared to FeSO₄ or untreated control. At 900 mg/L nFe₃O₄, chlorophyll-a, chlorophyll-b, and carotenoid increased by 27–55%, 108–126%, and 77–97%, respectively, compared to FeSO₄ applied at recommended field rate (1800 mg/L). Significant increments in bulb diameter (38–39%) and sister bulb number (300–500%) were observed upon 900 mg/L nFe₃O₄ treatment compared to FeSO₄ (1800 mg/L) and control. Furthermore, with 900 mg/L nFe₃O₄ treatment, total phenol, flavonoids, and Fe in bulbs increased by 27–46%, 29–73%, and 486–549%, respectively, compared to FeSO₄ (1800 mg/L). These findings demonstrate that bulb-priming followed by foliar application of 900 mg/L of nFe₃O₄ could significantly promote cellular adaptation, thereby improving photosynthetic efficiency, bulb yield, antioxidant activities, and Fe biofortification in shallot, and may serve as a novel approach for improving shallot production in semi-arid regions. Full article