SEARCH · Search PubMed

Results for “Computed Tomography Angiography”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

98 records · Page 6Linked to original sources

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Test-retest reliability of spatiotemporal, kinematic, and kinetic measures in marker-based 3D gait analysis: A systematic review.

BACKGROUND: Marker-based 3D gait analysis (3DGA) is widely used to quantify impairments and evaluate treatment effects. For longitudinal clinical interpretation, clinicians and researchers need reference values for inter-session measurement error. For this purpose, this systematic review synthesized Standard Error of Measurement (SEM) values for spatiotemporal, kinematic, and kinetic (moments) outcomes obtained from marker-based 3DGA studies. METHODS: PubMed and Scopus were searched (final search: 11 December 2025). Studies reporting inter-session test-retest SEM and/or MDC for steady-state overground or treadmill walking using marker-based motion capture were included. Two authors screened records and appraised methodological/reporting quality using a custom tool informed by COSMIN, GRRAS, and biomechanics-specific items. Due to heterogeneity, results were synthesized descriptively using study-level median SEM values, stratified by joint, plane, population (healthy, pathological, single subgroups), and walking condition. Minimal Detectable Change (MDC) values were computed for all available data. RESULTS: Thirty-four studies (762 participants, 44.2% females) were included, with substantially more evidence for overground than treadmill walking. Overground spatiotemporal outcomes showed low errors (walking speed SEM of 0.06 m/s; timing typically ≤0.03 s; spatial parameters generally ≤0.03 m). For joint kinematics during overground walking, median SEMs were 2.4° (sagittal), 1.9° (frontal), and 3.3° (transverse). The corresponding joint-kinetic SEMs were approximately 0.06, 0.04, and 0.03 Nm/kg, respectively. Treadmill data followed similar patterns. SIGNIFICANCE: Marker-based 3DGA allows for accurate assessment of spatiotemporal, kinematic, and kinetic gait features. We provided detailed SEM/MDC lookup tables to support clinical decision-making. Results further offer a benchmark for validating emerging gait assessment technologies (e.g., markerless systems) against realistic limits of marker-based 3DGA.

Humans↗

PubMed2026

Discovery of NAT-6-321056 as a novel modulator of VEGFR2 signaling to suppress tumor angiogenesis.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a master regulator of angiogenesis and cancer progression. However, current VEGFR2 modulators face significant challenges, including off-target toxicity and acquired resistance, underscoring the urgent need for novel therapeutic agents with improved efficacy and safety profiles. Here, we reported that virtual screening of 39,442 natural products from the ZINC natural products-derived library, coupled with molecular docking and molecular dynamics (MD) simulations to evaluate the binding stability of candidate compounds, identified NAT-6-321056 as a highly promising modulator of VEGFR2 signaling. Biological evaluations demonstrated that NAT-6-321056 exerted potent inhibition on the growth of a broad spectrum of cancer cells, including both solid tumors and hematological malignancies. In EA.hy 926 endothelial cells and SK-N-DZ neuroblast cells, the compound significantly suppressed proliferation, migration, and invasion. Microscale thermophoresis (MST) confirmed direct binding of NAT-6-321056 to VEGFR2 with favorable affinity. Kinase profiling against a panel of 33 kinases indicated that NAT-6-321056 exhibited a multi-kinase modulation profile. Mechanistic studies revealed that NAT-6-321056 suppressed the expression of hypoxia-inducible factor 1-alpha (HIF-1α) and was associated with reduced VEGFR2 phosphorylation and attenuation of the downstream ERK/JNK/AKT signaling pathways. Moreover, NAT-6-321056 exhibited robust in vivo anti-angiogenic effects in both the chick chorioallantoic membrane (CAM) assay and transgenic zebrafish vascular fluorescence imaging models. Computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction suggested acceptable drug-like properties. Collectively, these findings demonstrated that NAT-6-321056 is a promising modulator of VEGFR2 signaling with potent anti-angiogenic activity and represents a viable candidate for cancer therapy.

Vascular Endothelial Growth Factor Receptor-2↗

PubMed2026

Effectiveness of an AI-based home exercise app for rehabilitation of rotator cuff-related shoulder pain: A randomized controlled trial.

BACKGROUND: Rotator cuff-related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. OBJECTIVES: To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. DESIGN: Single-center, assessor-blinded, randomized controlled trial with two parallel groups. METHOD: Forty-six adults (mean age 59 years) with rotator cuff-related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. RESULTS: Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD -0.7; 95% CI -1.13 to -0.14 and MD -1.01; 95% CI -1.8 to -0.2, respectively). Upper limb function improved more at Week 4 (MD -7.3; 95% CI -12.3 to -2.2). FABQ scores decreased more at Week 12 (MD -7.6; 95% CI -14 to -0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). CONCLUSION: Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff-related shoulder pain.

Humans↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Long-term safety of oral orforglipron in Japanese participants with type 2 diabetes (ACHIEVE-J): a multicentre, randomised, open-label, parallel-group phase 3 trial.

BACKGROUND: Orforglipron, an oral GLP-1 receptor agonist, requires further evaluation in east Asian populations with type 2 diabetes, given this group's distinct pathophysiological characteristics. This study aimed to assess orforglipron as add-on treatment to diet and exercise alone or to oral antihyperglycaemic medications in Japanese participants with type 2 diabetes. METHODS: This multicentre, randomised, open-label phase 3 study was conducted in 40 medical research centres and hospitals in Japan. Adults with type 2 diabetes and elevated glucose levels managing their condition with diet and exercise alone or with one or two oral antihyperglycaemic medications were assigned (1:1:1) via computer-generated random sequence to receive once-daily oral orforglipron (3 mg, 12 mg, or 36 mg). Randomisation was stratified by background therapy, baseline HbA1c (≤8·5% or >8·5%), and metformin use (yes vs no; applied only to α-glucosidase inhibitors, thiazolidinedione, and glinides). Investigators, participants, and site staff were not masked to treatment. The primary endpoint was safety for 52 weeks, assessed in all randomly assigned participants who received at least one dose of orforglipron. This study is registered with ClinicalTrials.gov, NCT06010004 (ACHIEVE-J). FINDINGS: Between Sept 28, 2023, and June 5, 2025, 450 participants were screened and 401 were randomly assigned to three groups (3 mg, n=132; 12 mg, n=135; and 36 mg, n=134). 352 (88%) completed study treatment. 339 participants (85%, 95% CI 80·7-87·8) had at least one treatment-emergent adverse event (TEAE), more frequently in the 36-mg group (118 [88%, 95% CI 81·5-92·5]) than in the 3-mg (107 [81%, 73·5-86·8]) and 12-mg (114 [84%, 77·4-89·6]) groups. Most TEAEs were of mild (267 [67%, 61·8-71·0]) or moderate (63 [16%, 12·5-19·6]) severity. Discontinuations due to an adverse event occurred in 19 of 134 participants (14%, 95% CI 9·3-21·1) in the 36-mg group compared with seven of 132 (5%, 2·6-10·5) in the 3-mg group and 11 of 135 (8%, 4·6-14·0) in the 12-mg group. Across treatment groups, gastrointestinal symptoms were the most common TEAEs leading to study treatment discontinuation (3 mg: 5 [3·8%, 1·6-8·6]; 12 mg: 8 [5·9%, 3·0-11·3]; and 36 mg: 11 [8·2%, 4·7-14·1]). Level 2 (blood glucose <54 mg/dL) hypoglycaemia events occurred in three of 135 participants in the 12-mg group (2%, 0·8-6·3) and in three of 134 in the 36-mg group (2%, 0·8-6·4) groups. No level 3 (severe) hypoglycaemia events occurred. Outcomes were generally similar across background therapies. INTERPRETATION: Treatment with orforglipron in combination with diet and exercise alone or one or two oral antihyperglycaemic medications for 52 weeks demonstrated an acceptable safety profile in Japanese adults with type 2 diabetes. FUNDING: Eli Lilly. TRANSLATION: For the Japanese translation of the abstract see Supplementary Materials section.

Aged↗

PubMed2026

The glucagon and GLP-1 receptor dual agonist DD01 for metabolic dysfunction-associated steatotic liver disease and steatohepatitis (DD01-DN-02): 12-week results from a randomised, double-blind, multicentre, placebo-controlled, phase 2 trial.

BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is a major public health problem arising in the context of metabolic syndrome and obesity. DD01 is a liver-targeted GLP-1 receptor and glucagon dual agonist being investigated for the treatment of metabolic dysfunction-associated steatotic liver disease (MASLD) and MASH. The DD01-DN-02 trial aimed to evaluate the efficacy and safety of DD01 in adults with MASLD or MASH; this initial analysis reports prespecified 12-week outcomes to assess early hepatic effects. METHODS: DD01-DN-02 is an ongoing, randomised, double-blind, multicentre, placebo-controlled, phase 2 trial conducted at 12 outpatient clinical sites in the USA. Adults aged 18-70 years with obesity or who were overweight (BMI ≥25 kg/m2) were included in the study. Patients with MASLD or MASH underwent liver biopsy and MRI-proton density fat fraction (PDFF) and were eligible if liver fat content was 10% or higher with metabolic risk factors, or if the biopsy confirmed MASH with a non-alcoholic fatty liver disease activity score of at least 4. Participants were randomly assigned (1:1) to receive once-weekly subcutaneous DD01 40 mg or matched placebo over 48 weeks, dose-escalated over 2 weeks, using a centrally administered interactive response technology system. The randomisation sequence was computer-generated by an independent statistician. Participants, investigators, study staff, outcome assessors, and the sponsor were masked to treatment assignment. The primary endpoint was the proportion of participants having at least a 30% relative reduction in liver fat by MRI-PDFF at week 12, which was analysed in all randomly assigned participants receiving at least one dose of study drug or placebo. Safety analyses included all participants who received at least one dose of study drug. Missing primary endpoint data were handled using multiple imputation under a missing-at-random assumption. This trial is registered with ClinicalTrials.gov (NCT06410924) and is ongoing but closed to new participants. FINDINGS: Between June 13, 2024, and Jan 30, 2025, 67 eligible participants were enrolled, of whom 33 were randomly assigned to DD01 and 34 to placebo. The mean age of participants was 48·4 years (SD 10·6), 42 (63%) were female, 25 (37%) were male, 57 (85%) were White, and 52 (78%) participants had biopsy-confirmed MASH. At week 12, 25 (76%) of 33 participants receiving DD01 had a 30% or higher reduction in liver fat versus four (12%) of 34 participants receiving placebo (adjusted common odds ratio 28·8 [95% CI 7·2-115·2]; adjusted relative risk 6·3 [95% CI 2·5-15·9]; p<0·0001). Treatment-emergent adverse events occurred in 28 (85%) of 33 participants receiving DD01 and 23 (68%) of 34 participants receiving placebo. The most common adverse events were nausea (18 [55%] of 33 participants assigned DD01; six [18%] of 34 participants assigned placebo), diarrhoea (nine [27%] of 33; six [18%] of 34), and vomiting (ten [30%] of 33; four [12%] of 34). Treatment-emergent adverse events led to treatment discontinuation in four (12%) of 33 participants in the DD01 group and one (3%) of 34 participants in the placebo group. Two (6%) treatment-emergent serious adverse events occurred in the DD01 group (abdominal pain and acute cholecystitis) and zero in the placebo group. No deaths occurred. INTERPRETATION: In this prespecified 12-week primary analysis, DD01 produced rapid reductions in liver fat compared with placebo, supporting further evaluation in long-term studies. FUNDING: D&D Pharmatech, Neuraly.

Humans↗