Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging, among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
The application of chaos theory has positive results in different fields of science. Its nonlinear modeling properties and its vision of dynamic systems have enabled it to capture complex relationships in fields such as physics, financial econometrics, social systems and mathematical demography. This paper reviews the implication of chaos theory in the medical sciences. We carried out a systematic literature review under Cochrane’s international standards. A search strategy was executed with indexed terms (MeSH, DeCS and Emtree) that varied according to each database (Embase, MEDLINE, SciELO, LILACS). The PROSPERO registration number was CRD42023491407. In total, 2598 articles were retrieved, of which 20 were included. Algorithmic applications of chaotic systems were diverse. The medical fields with the largest studies were cardiology, neurology and oncology. The most used software was Matlab, however, in all cases, except one, we did not find open-source codes related to the studies. We found a wide heterogeneity in the studies reviewed, and this was reflected in the scope of research results. While some papers focus on proving the existence of chaotic behavior or understanding the nature of the phenomena being studied, others propose practical implications, such as in prescribing medicines and organizing health units. Not applicable. The online version contains supplementary material available at 10.1186/s42490-026-00111-0.
We present a novel acceleration scheme capable of accelerating electrons and ions in an underdense plasma. Transversely Pumped Acceleration (TPA) uses multiple arrays of counter-propagating laser beamlets that focus onto a central acceleration axis. Tuning the injection timing and the spacing between the adjacent beamlets allows for precise control over the position and velocity of the intersection point of the counter-propagating beam arrays. This results in an accelerating structure that propagates orthogonal to the direction of laser propagation. We present the theory that sets the injection timing of the incoming pulses to accelerate electrons and ions with a tunable phase velocity plasma wave. Simulation results are also presented which demonstrate 1.12 GeV proton beams accelerated in 3.6 mm of plasma and electron acceleration gradients on the order of 1 TeV/m in a scheme that circumvents dephasing. This work has potential applications as a compact accelerator for medical physics and high energy physics colliders.
Access to large, diverse biomedical datasets is critical for advancing medical research, yet privacy regulations severely restrict data sharing. We present an end-to-end framework for privacy-preserving health data synthesis that integrates advanced deep generative models (DGMs) with robust preprocessing, formal differential privacy (DP) training for select DGMs, empirical privacy risk evaluation, data-sufficiency analysis, domain-guided quality control, and biobank visualization tools. Released as open-source containerized software, the framework ensures reproducible deployment while preserving statistical fidelity, machine learning (ML) utility, and privacy guarantees. Empirical evaluations across diverse biobank datasets demonstrate that TabSyn-a transformer-based diffusion model-combined with our correlation-and distribution-aware CorrDst loss function achieves superior performance balancing fidelity, privacy, and computational efficiency. The tailored preprocessing pipeline effectively handles high missingness rates, substantially improving distributional accuracy and clinical plausibility. Across 26 biobank datasets spanning three regulatory levels, the framework shows that TabSyn with correlation- and distribution-aware loss function consistently achieves superior performance in terms of fidelity, privacy, and computational efficiency.
Early detection of right ventricular (RV) dysfunction is essential in pulmonary arterial hypertension (PAH) but remains challenging using conventional echocardiography. This study investigates the feasibility of a noninvasive, physics-based framework using three-dimensional (3D) echocardiography that integrates myocardial strain and volumetric flow analysis to characterize RV mechanical performance across stages of PAH. A prospective pilot study (N = 15) enrolled healthy controls, PAH patients with preserved RV size, and PAH patients with RV dysfunction. Deformation was evaluated by principal strain analysis and by conventional (longitudinal, circumferential) components. Hemodynamic metrics included hemodynamic forces and energetic properties that were derived using a physics-informed volumetric echocardiographic particle image velocimetry (V-Echo-PIV) method applied to contrast-enhanced acquisitions. Deformation analysis revealed that longitudinal strain was significantly reduced even in PAH patients with preserved RV dimensions, while second principal (secondary) strain showed a distinctive sign reversal, indicating a paradoxical systolic lengthening, early in the disease. The analysis of hemodynamic forces showed a marked reduction in systolic propulsion across all PAH stages. In contrast, energetic abnormalities were predominantly observed at later stage of the disease. The integration of 3D myocardial strain with fluid dynamics provides a comprehensive physiological assessment of RV remodeling. While strain and systolic propulsion appear as sensitive markers for early dysfunction, diastolic energetics may support disease staging. This noninvasive framework shows promise for early detection and longitudinal monitoring of PAH patients.
Changes in physiological pressures play a key role in the development and progression of human disease processes. Thus, the assessment of pressures within blood vessels and other bodily compartment is crucial in the diagnosis and management of multiple medical conditions. Presently, techniques for pressure measurement are invasive or have limited accuracy and scope of assessment. Utilizing the subharmonic signal of ultrasound contrast agents offers a promising solution that could address these limitations. After the initial development of this technology in the late nineties, further investigation has brought subharmonic pressure estimation from in vitro exploration to attempts at clinical implementation. However, lack of availability of subharmonic imaging on most clinical scanners, and variability of subharmonic response with different contrast agents have impeded clinical acceptance and widespread use of this modality. This review examines subharmonic imaging and the use of ultrasound contrast agents for estimating physiological pressures, particularly in the heart and portal venous system. A focus is placed on clinically relevant physiologic pressures and their existing measurement approaches, the physics of subharmonic signal generation, in vitro studies demonstrating key findings, and more recent clinical trials. The review also highlights present limitations and future research directions that may help advance clinical translation.
Single-fraction stereotactic ablative radiotherapy (SABR) is a curative treatment option for patients with early lung cancer. We undertook a feasibility study to assess whether simulation, planning and treatment could be undertaken within a single day and whether this expedited pathway was acceptable to patients. A multidisciplinary team of radiation oncologists, radiation therapists and medical physicists developed a workflow to permit all aspects of planning and treatment to be conducted within a single working day. All aspects of the pathway were timed. Prescription dose was 30 Gray. Patients completed an Experience Survey within two weeks of treatment. The study would be deemed successful if it met both dual primary endpoints of feasibility (7/10 patients treated within 8 working hours) and patient satisfaction (7/10 expressing overall satisfaction). Ten patients were treated. These included 6 women and 4 men. Median age was 76.5 and 9 patients had been deemed to be medically inoperable. Median time from commencement of CT simulation to end of treatment was 6 h 50 min (IQR 6 h 42 m - 7 h 4 m); 9/10 patients were treated within the target 8-hour window. All 10 patients expressed overall satisfaction with the service. All 10 felt it was more convenient than our usual treatment pathway would have been. Same-day single-fraction SABR is feasible and acceptable to patients. This pathway should be considered for those who live a significant distance from treatment centres or who have other difficulties in attending for multiple visits.
Targeted delivery of drugs and hyperthermia in cardiovascular disease demand the accurate delivery of nanoparticles in complex arterial geometries. This paper introduces combined hybrid computational model that concomitantly examines the combined impact of nanoparticle radius and interparticle spacing on the thermal and mass transport characteristics of ternary bio-nanofluid flow under magnetohydrodynamic (MHD) effect. The ternary fluid is composed of blood fluid with suspended nanoparticles such as gold (Au), silver (Ag) silica (SiO2). The mathematical model accounts for geometric properties of nanoparticles such as nanoparticles radius and interparticle spacing for their practical utility for several medical interventions. The numerical analysis is based on hybrid computational strategy, where the solutions are determined through the bvp4c numerical solver and then a novel supervised multi hidden layers Artificial neural network (ANN) is integrated. The proposed model has a high predictive capability with an exceptionally high accuracy with the lowest Mean squared error and ideal regression coefficient MSE=9.6327×10-11, Gradient=9.5681e-08, Mu=1e-09, and R2=1.0. Some of the main findings indicate that less spacing between particles (h=0.1) leads to continuous networks of thermal percolation, which enhance the thermal conductivity by up to 35% to improve the efficiency of hyperthermia, whereas the larger nanoparticles (radius ≥1.5) offer a higher drug-loading capacity, yet the rate of heat transfer decreases by 15-20%. Optimization of the magnetic parameter (M=0.1-0.7) also decreases flow velocity by 28% and extends the nanoparticle residence time at the stenosis by 35% which allows sustained drug delivery, results directly applicable to clinical-strength (1.5-3T) MRI-guided interventions. Radiation parameter (Rd=0.5-2.5) increases temperature of the arteries by 15-20% giving controllable thermal modulation to applications of hyperthermia. The proposed model predicts that optimal nanoparticle preparations (50 nm radius, 20 nm spacing) have to potential to lower the rate of restenosis by 30-40% in relation to traditional drug-eluting stents. The purpose of such an integrated computational-machine learning systems is to give quantitative advice to stent coating design, nanoparticle formulation, and optimization of treatment protocols, and has been directly used in biomedical interventions. The results can be used to offer practical advice to stent manufactures, interventional radiologist and pharmaceutical developers to create evidence-based cardiovascular therapy of the next generation.
IntroductionLow- and middle-income countries (LMICs) like Nigeria face rising cancer incidence and mortality, with late-stage presentation and limited resources. Only eight government-funded radiotherapy centres serve a population of 223.8 million-far below the estimated 280 radiotherapy machines required. To increase patient throughput we evaluated integration of AI auto-contouring tools to expedite treatment planning, specifically target and organ-at-risk delineation.Materials and MethodsWe performed an observational, survey-based study of radiation oncology staff at our Cancer Centre. Participants were consultant and resident oncologists and medical physicists. The survey compared time spent using AI auto-contouring versus manual contouring and collected perceptions of impact, benefits, and limitations.ResultsThirty-one staff responded: 20 (64.5%) oncologists and 11 (35.5%) medical physicists. Experience with AI varied (33% ≤ 6 months; 13% ≈2 years). Respondents reported increased confidence in planning: 11 (35%) moderate, 12 (39%) moderate-high, and 8 (26%) high. Common limitations were licence availability (20, 64.5%) and technical expertise (19, 61.3%). Most respondents (20, 65%) would recommend the tool. The principal benefit was improved workflow efficiency (25, 81%). AI-assisted planning significantly reduced planning time for most tumour sites; sites with complex anatomy showed no time benefit, reflecting the need for intensive manual correction.ConclusionDeployment of AI auto-contouring at a Nigerian cancer centre reduced planning time for most sites and improved clinician confidence, but complex anatomical regions still require detailed manual oversight and additional AI training. AI tools can increase throughput in LMIC radiotherapy services, though licensing, infrastructure, and training barriers exist and must be addressed to ensure safe implementation. Future work should include multi-centre validation, formal inter-rater reliability assessment, and prospective patient-level outcome evaluation and cost-effectiveness analyses.
The establishment of monoclonal, stably transduced cell lines is a critical step in functional genomics and drug discovery. However, conventional methods are often time-consuming, labor-intensive, and prone to compromising cell viability. Here, we present a microfluidic single-cell sorting system based on laser-induced jetting (LIJet) that significantly improves the efficiency and quality of stable cell line generation. This system integrates a light-responsive substrate with metal coating and a PDMS microfluidic chip featuring an array of microwells, enabling single-cell capture, identification, and non-contact precision release. A 532 nm nanosecond pulsed laser is used to generate localized microjets, which accurately eject target cells from the microwells. In addition to achieving a 100% sorting success rate and maintaining over 95.3% post-sorting cell viability, the system supports long-term on-chip culture and viral transduction with full real-time monitoring. We demonstrated the platform's functionality by performing on-chip ZsGreen lentiviral transduction of human lung adenocarcinoma PC9 cells, followed by fluorescence-based single-cell selection, ultimately establishing monoclonal cell lines with stable transgene expression. This platform offers notable advantages in low-damage manipulation, dynamic monitoring, and functional perturbation, providing a robust and efficient solution for the construction of stably transduced cell lines, gene function screening, and phenotypic analysis across a variety of biomedical applications.
Quantification using the Centiloid (CL) scale has become a valuable information to consider when interpreting amyloid-PET images and is now implemented in several software packages. This work aims to assess the comparability of CL from [18F]flutemetamol scans derived using several research and commercial quantification pipelines. This analysis relies on three datasets: a test-retest cohort, a group of clinically relevant patients with amnestic mild cognitive impairment (aMCI) and a subgroup from the BioFINDER-1 cohort enriched with scans with amyloid loads around potential clinical decision thresholds (0-50CL). Images from the Test-Retest and aMCI cohorts were processed across seven quantification pipelines: three commercial software platforms and four research tools, including the standard SPM8 workflow. The statistical analysis was based on three steps: 1) a repeatability analysis using the test-retest data; 2) a reproducibility analysis across all pipelines using the aMCI cohort; 3) an inter-software reliability analysis around three clinically relevant thresholds: 11, 25 and 37 CL using the aMCI and the BioFINDER-1 data. In the Test-Retest dataset composed of 10 Alzheimer's Disease (AD) patients, high test-retest repeatability and reliability were observed with an absolute bias of less than 5 CL. Within-individual coefficients of variation ranged from 2.6 to 4.4% and repeatability coefficients from ∼8 to ∼16 CL. CL quantification was generally reproducible across pipelines in a dataset of 80 aMCI individuals (R2 in [0.94-0.99], slope in [0.98-1.03], intercept in [-4, 4], but the 95% limits of agreement (LoAs) ranged between ∼±12 and ∼±21 CL. Agreement between software around the three clinically relevant thresholds was 92-100% (kappa 0.83-1) in the aMCI data (N = 80) and 75-99% (kappa 0.48-0.96) in the BioFINDER-1 subgroup (N = 110). In this study, CL quantification was shown to be robust across a range of currently available software platforms. Uncertainty estimates should always be considered when interpreting results. In clinical practice, the choice of quantification software should not impact patient management decisions.
Brain tumors remain a major clinical challenge, particularly in assessing treatment response after radiotherapy. The aim of this study was to evaluate the effectiveness of noninvasive spectroscopic MRI techniques in monitoring brain tumor response to radiotherapy by analyzing longitudinal changes in metabolic biomarkers. This observational longitudinal study was conducted from October 1, 2024, to June 1, 2025, in Erbil, Iraq, using purposive sampling. Patients with primary brain tumors who underwent postoperative radiotherapy at Awat Center were included, with MRI and 3D ^1H-magnetic resonance spectroscopy scans performed at Bawan Diagnostic Center at pretreatment, post-treatment, and follow-up stages. Key biomarkers (choline, creatine, N-acetylaspartate, and lactate) and their ratios were analyzed using repeated measures analysis of variance, Bonferroni post hoc tests, receiver operating characteristic analysis, and multivariate logistic regression. Statistical analysis was performed using Stata version 12 (StataCorp LLC, College Station, TX). A total of 16 patients were included in the study. The most significant biomarker change was a reduction in choline, indicating decreased tumor proliferation across time points. Lactate-to-creatine ratios also declined, reflecting reduced anaerobic metabolism. Receiver operating characteristic analysis identified choline and lactate reductions as the most predictive indicators of treatment response. The final regression model showed that higher Karnofsky Performance Status was significantly associated with better treatment outcomes. Biomarker-driven risk stratification further supported clinical decision making by identifying thresholds for continued therapy versus reassessment. Noninvasive spectroscopic MRI techniques proved effective in detecting metabolic changes in brain tumors after radiotherapy, especially reductions in choline and lactate, which were associated with clinical treatment response. Based on these findings, policymakers and healthcare providers are encouraged to integrate magnetic resonance spectroscopy into routine neuro-oncology imaging protocols and support specialized training for radiologists.
ASTRO's 2018 guidelines for hypofractionated whole-breast irradiation (HF WBI) recommend limiting high-dose regions to improve dose homogeneity and minimize toxicity. However, correlation of these guidelines with clinical outcomes remains limited. This study investigates the relationship between dose-volume parameters and acute toxicities including pain, erythema, edema and moist desquamation in patients treated with HF WBI in accordance with ASTRO 2018 guidelines. We retrospectively reviewed patients treated from 2018 to 2023 with HF WBI (42.56 Gy in 16 fractions or 40.05 Gy in 15 fractions) between 2018 and 2023 using 3-dimensional conformal field-in-field planning. All plans adhered to ASTRO constraints (V105% < 200 cm3; V107% < 2 cm3). Acute toxicities were prospectively scored using the Common Terminology Criteria for Adverse Events V5.0 at weekly on-treatment visits (OTVs) and at 1 month follow-up. Logistic regression analyses identified predictors of grade ≥ 2 toxicities. 600 patients were treated with HF WBI. Of those patients, 82.3% received a dose of 42.56 Gy in 16 fractions, whereas the remaining received 40.05 Gy in 15 fractions. Three-dimensional field-in-field planning was used in all patients and 73.7% received a boost. Moderate-to-severe pain (grade ≥ 2) occurred in 29.7% of patients during treatment despite compliance with ASTRO guidelines. In contrast, grade ≥ 2 erythema, edema, and moist desquamation were observed in <5% of patients. Multivariate analysis identified V105 % (cm3) ≥ 50 cm3, V105 % (%) ≥ 5% and body mass index ≥ 30 as independent predictors of grade ≥ 2 pain, which decreased by 30% when V105 % (cm3) < 50 cm3 and by 18.5% when V105 % (%) < 5%. Although skin related toxicities were infrequent and largely resolved by 1 month, pain remained a prevalent side effect of HF WBI. These findings suggest that ASTRO's current V105% thresholds may be too permissive for pain mitigation. Stricter limits of V105% (cm3) < 50 cm3 and V105% (%) < 5% may better protect patients from treatment-related discomfort and warrant consideration in future guideline updates.
Globally, 537 million persons live with diabetes, and a lifetime risk of up to 34% of developing diabetic foot ulcers (DFUs) necessitates strengthened preventive initiatives. The study aimed to develop and evaluate a clinical decision support system (CDSS) to be used by health care professionals in foot assessment and risk stratification as a base for prevention. Based on principles of human-computer interaction, the CDSS was developed for DFU risk assessment. Users, health care professionals from Region Västra Götaland in Sweden, evaluated the functions regarding effectiveness, efficiency, and satisfaction using a mixed methods usability testing approach. Expectations and experiences of using the CDSS were evaluated with the System Usability Scale (SUS). A total of 9 participants participated. User expectations of the CDSS, measured by SUS, averaged 77.2 (SD 14.6). Posttest SUS scores were 68.9 (SD 14.3), with a mean difference of 8.3 (P=.07), a nonsignificant reduction of usability after testing. The effectiveness of the CDSS in supporting users to complete 9 clinical tasks showed that for 7 (78%) tasks, at least 5 (56%) testers successfully achieved the intended goals. Tasks involving the identification of ingrown toenails and the confirmation of foot status, including risk stratification for the patient, were completed by fewer testers. Efficiency, measured as mean task completion time, ranged from 7 seconds to 9 minutes 20 seconds, and qualitative feedback informed recommendations for further system refinement. Users reported that a structured CDSS has the potential to support more equitable, consistent, and person-centered DFU prevention within a digital health service. A digital health service for DFU risk stratification was developed based on national and international guidelines. Although the users' expectations of the usability were higher compared to how they experienced the CDSS, the SUS test was near a threshold of 70, indicating that the system being tested was above average in usability. Further development and validation, both nationally and internationally, with continued attention to users' needs and contextual factors, are recommended.
Arabidopsis thaliana from the Chernobyl Exclusion Zone showed altered stress responses, reduced germination, and genomic signatures of microevolution affecting DNA repair, redox signalling and cell-cycle-related genes. Chronic exposure to ionising radiation (IR) in the Chernobyl Exclusion Zone (CEZ) has created a field experiment for plant evolution. We collected Arabidopsis thaliana seeds from a reference plot (Babchin) and two radioactively contaminated plots (Vygrebnaya Sloboda and Masany), established in vitro seed lines for each plot and studied their physiology and genomes. Seeds were challenged with acute γ-irradiation (150 Gy), heat (50 °C) and oxidative stress (0.01 µM methyl viologen). The radiation legacy manifested as contrasting stress response profiles and suppressed germination in chronically irradiated lines, which was rescued by exogenous ROS. Genome sequencing of plants from the heavily contaminated plot, Masany, revealed decreased nucleotide diversity and signs of a selective sweep, accompanied by increased fixation rates for single-nucleotide polymorphisms (SNPs) in exons. Compared with the non-irradiated reference population, genes that accumulated unique SNPs in Masany were related to DNA repair, cell cycle and mitosis, phragmoplast assembly, response to oxidative stress, Ca2+ and ROS signalling, and epigenetic processes. Together, the data show that decades of low-dose irradiation drive rapid microevolution in A. thaliana, favouring mutations that bolster genome stability and stress-signalling networks whilst probably compromising seed performance. These findings provide the first field-scale genomic evidence of the targeted accumulation of mutations in specific genomic regions of chronically irradiated plants, suggesting that long-term exposure to chronic ionising radiation may alter population genetic structure.
Current intravital imaging techniques for the mouse central nervous system (CNS) do not simultaneously provide micrometer-scale spatial resolution, whole-brain coverage, and sub-minute temporal resolution, limiting organ-wide interrogation of CNS fluid dynamics in vivo. Here, we introduce intravital synchrotron radiation-based hard X-ray micro computed tomography (SRµCT), a modality that enables dynamic whole-brain imaging at micrometer-scale spatial resolution in living mice. We performed intravital SRµCT of mouse CNS fluid spaces at three synchrotron radiation facilities, imaging both anesthetized free-breathing and mechanically ventilated animals, with and without retrospective cardiac gating. This approach achieves complete brain coverage with temporal resolution of up to 23 s and voxel sizes down to 6.3 µm, at an effective spatial resolution better than 20 µm, enabling time-resolved visualization of cerebrospinal fluid (CSF) contrast distribution and quantitative analysis of tissue motion across the entire brain. By combining micrometer-scale resolution, whole-organ field of view, and dynamic intravital imaging, SRµCT closes a long-standing methodological gap between optical microscopy and magnetic resonance imaging. Intravital SRµCT provides access to spatiotemporal information that cannot be obtained with existing techniques and establishes a framework for testing and integrating mechanistic models of CSF dynamics and solute transport at the scale of the whole brain.
This study presents a comprehensive simulation-based assessment of potential transboundary radiological transport to Ireland from six nuclear facilities in the United Kingdom and France, utilising weather data over a fourteen-year period (2011-2024). Systematic screening of 2.2 million HYSPLIT atmospheric dispersion simulations identified eighteen worst-case scenarios representing maximum ground deposition, maximum air concentration, and minimum warning time. Independent verification using FLEXPART and HYSPLIT demonstrated expected inter-model variability (factor of 1-10), with both Lagrangian models providing consistent risk assessment brackets. Heysham, despite its complex 19-isotope AGR source term, produced negligible radiological doses to Ireland (<0.01 mSv), substantially below intervention thresholds. More distant continental facilities (Flamanville, Paluel, Sizewell B) showed low but measurable doses (0.1-4.6 mSv), remaining well below the 50 mSv sheltering threshold. This study addresses urgent-phase protective actions only; transitional-phase food chain countermeasures are beyond scope. Hinkley Point C (under construction) showed elevated but sub-threshold doses (0.3-8.5 mSv). However, the cancelled Wylfa Newydd gigawatt-scale project (the site is now proposed for small modular reactors), owing to its extreme proximity to Ireland, exhibited concerning dose predictions: FLEXPART calculated 19.6 mSv under maximum deposition conditions (May 2024 scenario), approaching the 50 mSv sheltering threshold, whilst HYSPLIT predicted 4.5 mSv. This inter-model variability (factor of ∼5) highlights genuine uncertainty for near-source impacts but converges on a critical finding: were a gigawatt-scale reactor constructed at the Wylfa site, severe accidents during specific meteorological patterns could require protective actions in Ireland. Machine learning models (XGBoost) achieved validation accuracies of 85-93% for rapid impact prediction, whilst global sensitivity analysis revealed that meteorological conditions, rather than release parameters, dominate consequence severity. These findings provide quantitative assurance that existing nuclear infrastructure poses low transboundary risk to Ireland well below urgent-phase intervention thresholds (sheltering and evacuation), whilst demonstrating that facility proximity constitutes the dominant factor determining potential radiological impact.
The extensor mechanism of the knee is essential for maintaining normal daily function and for activities such as walking and athletic performance. It comprises the quadriceps muscles and tendon, patella and peripatellar fat pads, patellofemoral joint and retinacula, patellar tendon, and tibial tuberosity. Disruption of any component can result in pain, instability, or loss of active extension. Owing to its superficial location and continuous biomechanical loading, it is particularly vulnerable to acute trauma, repetitive microtrauma, chronic degeneration, and traction apophysitis in the pediatric population. Imaging is essential for accurately differentiating between diagnoses and guiding management. Radiographs remain indispensable for assessing patellar height, fracture morphology, and skeletal variants, while computed tomography (CT) is a reliable preoperative tool for defining fracture configuration, comminution, and articular involvement. Ultrasound (US) offers dynamic, high-resolution evaluation of tendons and retinacula, facilitating differentiation of partial from complete tears and assessment of postoperative integrity. Magnetic resonance imaging (MRI) provides comprehensive characterization of soft tissues, bone marrow, cartilage, and associated intra-articular pathology, and is central to preoperative planning in complex injuries and patellofemoral instability. In addition to diagnosis, imaging findings directly influence treatment decisions, including fracture fixation strategies, tendon repair or augmentation, and treatment selection. Postoperative imaging plays a vital role in monitoring healing, detecting complications such as construct elongation, re-tear, hardware irritation, and recurrent instability, and guiding rehabilitation. This review highlights the radiological anatomy and injury patterns of the knee extensor mechanism, emphasizing that integrated use of radiographs, ultrasound, and MRI enables accurate, clinically relevant assessment.
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Dopaminergic neurons within the ventral tegmental area (VTA) is involved in the development of cognitive dysfunction during chronic pain. It is controlling cognitive behaviors via its various projection pathways to different areas in the brain including lateral hypothalamus (LH). The activation of LH induced analgesia during chronic pain. In the current study, we assessed whether D2 dopamine receptors activity within the VTA can change the induced-cognitive responses of LH following its cholinergic stimulation during neuropathic pain in rats. Aditionally, we assessed whether cholinergic stimulation of LH can change firing rate of VTA neurons. Male Wistar rats were implanted with two separate cannulae into the LH and VTA on the same side. After 4 days' recovery of cannulation surgery, animals underwent second surgery for induction of neuropathic pain (chronic constriction injury of sciatic nerve, CCI). Citicoline (1 µg/1µl normal saline), as an acetylcholine precursor, that activates the LH projecting neurons, were microinjected into the LH. In the other groups, D2-like dopamine receptor antagonist, sulpiride (1 µg/1µl normal saline) or agonist, bromocriptine (1 µg/1µl normal saline) were microinjected into VTA, 5 min prior intra-LH injection of citicoline. Cognitive and electrophysiology studies were assessed 15-30 minutes after drug injection. Stimulation of LH via citicoline significantly induced analgesic and learning/memory enhancer effects. Additionally, intra-VTA injection of bromocriptine significantly increased analgesic and learning/memory enhancer effects of LH. Interestingly, sulpiride significantly decreased analgesic and learning/memory enhancer effects of LH. Stimulation of LH via citicoline effectively increased firing rate of VTA neurons. Intra-VTA injection of bromocriptine and sulpiride significantly increased and decreased firing rate of VTA neurons, respectively. The present study suggest that the analgesic and memory enhancer effects which produced by LH cholinergic stimulation is mediated by D2-like dopamine receptors of VTA in neuropathic pain. Additionally, the cholinergic stimulation of LH via citicoline can increase firing rate of VTA neurons during neuropathic pain and intra-VTA injection of sulpiride effectively reversed the effects of citicoline on firing rate of VTA neurons.