SEARCH · Search PubMed

Results for “Multimodal imaging”

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.

251 recordsLinked to original sources

PubMed2026

Dual-Reporter Gene-Based Multimodal Imaging for Tracking Mesenchymal Stem Cells in Diabetic Skin Wound Repair.

BACKGROUND: Diabetic foot ulcer (DFU) is a clinically challenging complication characterized by poor healing outcomes, and conventional therapies provide limited benefit. Mesenchymal stem cell (MSC) transplantation offers a promising strategy for DFU repair. However, the low survival of transplanted MSCs in the hostile wound microenvironment, coupled with the lack of real-time, non-invasive methods to track these cells in vivo, severely hampers their therapeutic efficacy and clinical translation. METHODS: We engineered MSCs to co-express a dual reporter system comprising near-infrared fluorescent protein (iRFP) and ferritin heavy chain (FTH1). These modified cells were then integrated with a fibrin glue (FG) scaffold to create a unified platform that supports both multimodal imaging and therapeutic function within skin wounds. First, FTH1 overexpression enhances the antioxidant capacity of MSCs, while the FG scaffold provides structural support; this combination enhances cell survival and retention. Second, the iRFP/FTH1 dual reporter enables near-infrared fluorescence imaging and MRI-based localization, establishing a multimodal platform for real-time cell tracking. RESULTS: In a full-thickness skin defect model in diabetic mice, multimodal imaging revealed that transplanted cells persisted in the wound area for approximately seven days. Treatment with iRFP/FTH1-MSCs/FG significantly accelerated wound closure and promoted hair follicle regeneration and angiogenesis. Additionally, local iron deposition resulting from FTH1 expression enhanced fibroblast migration and collagen synthesis, further facilitating extracellular matrix remodeling. Mechanistic studies demonstrated that this therapy drives macrophage polarization toward the anti-inflammatory M2 phenotype and activates the PI3K-AKT-VEGF signaling pathway. These complementary effects synergistically enhance tissue regeneration and systematically improve diabetic wound healing. CONCLUSIONS: Collectively, this multimodal stem cell-scaffold system effectively integrates dynamic cell tracking with stem cell therapy during skin wound repair. It addresses a critical technical gap in visualizing stem cells within the wound microenvironment and provides valuable methodological and theoretical foundations for optimizing regenerative strategies for diabetic skin wounds.

Animals↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Combining neuromelanin-sensitive MRI and quantitative susceptibility mapping for enhanced diagnosis and differentiation of parkinson's disease: A systematic review.

BACKGROUND: Loss of dopaminergic neurones and iron deposition in the substantia nigra pars compacta (SNpc) are two major pathological hallmarks of Parkinson's disease (PD). Such changes can be visualised by advanced techniques including neuromelanin-sensitive MRI (NM-MRI) and quantitative susceptibility mapping (QSM). This systematic review investigates the diagnostic performance and methodological development of the integrated use of NM-MRI and QSM in PD. METHODS: The systematic search was performed in four databases (Scopus, PubMed, ScienceDirect, and Web of Science) according to the PRISMA 2020 guidelines until July 2026. Bias was assessed using QUADAS-2 and certainty of evidence was assessed using GRADE. RESULTS: Seventeen studies with 2228 participants were included. Combined NM-MRI and QSM consistently showed reduced neuromelanin volume/contrast and increased iron deposition in the SNpc of PD patients compared to healthy controls. Multimodal integration yielded a significant improvement in diagnostic accuracy (AUC values 0.86-0.99), and was able to successfully differentiate PD. Recent methodological advances included simultaneous acquisition sequences (e.g. MTC-GRE, STAGE, setMag) and AI-driven automated segmentation, which led to significantly reduced scan times and improved reproducibility. CONCLUSION: The combination of NM-MRI and QSM has a synergistic effect and provides powerful complementary biomarkers for the diagnosis and differential diagnosis of PD.

Humans↗

PubMed2026

Neurocorrelates of nocturnal enuresis in pre-adolescent children.

INTRODUCTION: Nocturnal enuresis (NE) is a common neurodevelopmental condition, yet its underlying neural mechanisms remain unclear. This study leverages the large-scale Adolescent Brain Cognitive Development (ABCD) dataset to identify structural and functional brain correlates associated with active symptoms and the resolution of bedwetting. METHODS: Using cross-sectional data from 3472 participants aged 9-10 years, children were categorized into three groups: active nocturnal enuresis (ANE, n = 225), history of nocturnal enuresis (HNE, n = 1171), and healthy control groups (CG, n = 2076). Multimodal neuroimaging protocol evaluated macrostructural properties via structural MRI (sMRI), microstructural white matter integrity via diffusion MRI (dMRI), and functional connectivity via resting-state fMRI (fMRI). Group differences were evaluated using linear models within an ANCOVA framework, adjusting for intracranial volume and handedness with False Discovery Rate (FDR) correction. RESULTS: Compared to controls, the ANE group exhibited a significant volume deficit in the right caudate, decreased sulcal depth in the left insula, and lower internal correlation within the Cingulo-Opercular Network (CON). Conversely, the dry HNE group demonstrated significant structural adaptations, including bilaterally larger putamen volumes and increased right caudate volume compared to the ANE group. The HNE group also showed increased microstructural density (decreased mean diffusivity) in the bilateral hippocampus and an increased cortical surface area in the left insula. Both NE groups demonstrated persistently reduced functional coupling within the CON. CONCLUSIONS: Nocturnal enuresis appears to be associated with a potential complex central signaling deficits. Reduced internal correlation within the CON across both active and former bedwetters indicates a potential for impairment in processing internal homeostatic bladder signals during sleep.

Humans↗

PubMed2026

Treatment-related associations of nucleus accumbens connectivity within mesocorticolimbic circuits in depression.

Pharmacological treatment remains a mainstay in managing depression, yet the neural correlates associated with treatment response remain incompletely understood. This study used multimodal neuroimaging to examine nucleus accumbens (NAc)-centered structural and functional alterations associated with fluoxetine and Shugan Jieyu Capsule (SG), a traditional Chinese medicine, in patients with mild-to-moderate depression (MMD). Sixty patients were randomized to an 8-week course of fluoxetine or SG. Depression severity was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24), and structural and functional MRI scans were acquired at baseline and endpoint. Both treatments were associated with significant symptom improvement. Neuroimaging analyses revealed structural and functional alterations involving the NAc. Changes in NAc-amygdala connectivity showed an exploratory association with symptom improvement in the SG group, whereas changes in NAc-rostral anterior cingulate cortex connectivity were associated with symptom improvement in the fluoxetine group and remained significant after correction for multiple comparisons. In addition, remitters exhibited stronger baseline connectivity between the NAc and ventral tegmental area and between the NAc and middle frontal gyrus compared with non-remitters. These findings suggest that NAc-centered connectivity may be relevant to treatment-related neural changes in depression and may inform future research on imaging-based candidate markers of treatment response and personalized treatment approaches. TRIAL REGISTRATION: The study is registered in https://www.chictr.org.cn/ with a registration number ChiCTR1900024988 (date: 08.06.2019).

Humans↗

PubMed2026

Current Concepts and Emerging Technologies in Aesthetic Outcome Assessment of Breast Reconstruction.

Aesthetic outcomes are a crucial determinant of the overall success of breast reconstruction. Recently, aesthetic assessment has evolved from relying mainly on subjective impressions to incorporating more quantitative methods. This systematic review summarizes current concepts and emerging technologies in aesthetic outcome assessment after breast reconstruction. A comprehensive search of studies evaluating aesthetic outcomes following implant-based, autologous or hybrid breast reconstruction was performed between 2000 and 2025. Assessments were classified as subjective or objective. Extracted variables included assessment characteristics, aesthetic outcome domains, and patient-centered outcomes. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Levels of evidence were classified according to the Oxford Centre for Evidence-Based Medicine. A total of 51 studies involving 7711 participants from 16 countries were included. Subjective tools were most frequently employed, led by the BREAST-Q (35/51, 69%), followed by expert- or panel-based evaluations (17/51, 33%) and the visual analog scale (2/51, 4%). Objective methods were applied in 19 studies and included 3-dimensional surface imaging (8/51, 16%), BCCT.core (7/51, 14%), eye tracking (3/51, 6%), and artificial intelligence-based analyses (3/51, 6%). Although subjective tools captured satisfaction with breast appearance, objective tools quantified morphological parameters and positional landmarks. BREAST-Q remains the cornerstone of outcome evaluation after breast reconstruction, providing patient-centered perspectives, including, but not limited to, aesthetic perception. A progressive shift toward multimodal evaluation was noticed, as no single modality comprehensively addressed all aesthetic domains. Future research should focus on integrating subjective and objective assessment methods within a unified framework. Level of Evidence: 3 (Therapeutic) For image description, please refer to the figure legend and surrounding text.

Humans↗

PubMed2026

Glymphatic dysfunction mediates inflammation-driven vascular burden and cognitive decline in cerebral small vessel disease.

BACKGROUND: Cerebral small vessel disease (CSVD) is increasingly recognized as a disorder involving microvascular dysfunction, impaired perivascular clearance, and inflammatory processes. However, how systemic inflammatory burden, neurovascular coupling (NVC), glymphatic MRI markers, vascular lesion burden, and cognition are interrelated remains unclear. MATERIALS AND METHODS: In this prospective study, 155 patients with CSVD and 70 healthy controls (HCs) underwent multimodal MRI. NVC was quantified using the cerebral blood flow/fractional amplitude of low-frequency fluctuations ratio. Glymphatic function was assessed via the diffusion tensor image analysis along the perivascular space (ALPS) index, choroid plexus volume (CPV), and perivascular space (PVS) fractions. Structural equation modeling (SEM) was employed to evaluate the direct and indirect effects of inflammatory markers on vascular burden and cognitive performance. RESULTS: Patients with CSVD exhibited significantly diminished NVC (specifically in the right median cingulate and left frontal gyri) and impaired glymphatic function (lower ALPS-index; higher CPV and PVS fractions) compared to HCs. SEM revealed that inflammatory biomarkers exerted both a direct effect on vascular burden and a substantial indirect effect (accounting for 66.3% of the total effect) mediated through two pathways: a single-mediation path via glymphatic function (42.8%) and a serial-mediation path via NVC and glymphatic function (23.5%). Increased vascular burden was significantly associated with poorer cognitive performance. CONCLUSION: Inflammation drives CSVD progression and cognitive decline primarily through the disruption of NVC and glymphatic clearance mechanisms. These findings highlight glymphatic dysfunction as a critical mediator of inflammation-related structural brain damage.

Humans↗

PubMed2026

Clinical Utility of Ultra-Widefield Swept-Source OCT for Intraocular Tumors: Comparison With Ultrasonography, SD-OCT, and MRI.

PURPOSE: To evaluate the clinical performance of ultra-widefield swept-source optical coherence tomography (UWF-OCT) in the assessment of choroidal tumors and to compare it with ultrasonography (US), spectral-domain (SD)-OCT, and magnetic resonance imaging (MRI). DESIGN: Retrospective diagnostic comparison. SUBJECTS: Thirty-nine eyes from 39 patients diagnosed with choroidal tumors at a single tertiary referral center. METHODS: This retrospective diagnostic comparison evaluated patients diagnosed with choroidal tumors at a single tertiary referral center between January 2023 and August 2025. All patients underwent UWF-OCT imaging at diagnosis. Tumor measurements obtained with UWF-OCT were compared with US, SD-OCT, and MRI. Comparative analysis among imaging modalities and predictors affecting UWF-OCT applicability was performed. MAIN OUTCOME MEASURES: Tumor thickness (mm) and largest basal diameter (LBD, mm) measurements, and complete measurability rate across different tumor size categories. RESULTS: Thirty-nine eyes from 39 patients (mean age 59.2 ± 16.9 years) were analyzed, including 27 choroidal melanomas (69.2%), 5 metastatic tumors (12.8%), 4 hemangiomas (10.3%), 2 osteomas (5.1%), and 1 (2.6%) indeterminate choroidal melanocytic lesion. UWF-OCT successfully measured both tumor thickness and largest basal diameter (LBD) in 100% (31/31) of small and medium choroidal tumors, substantially outperforming SD-OCT (complete measurement achieved in 63.6% of small tumors, and 0% of medium or large tumors). UWF-OCT measurements were systematically smaller than ultrasonography (thickness: -32.3%, P < .01; LBD: -11.1%, P < .01) and MRI (thickness: -29.2%, P < .01). Mushroom-shaped tumor morphology was the strongest negative predictor of UWF-OCT quality (OR = 0.015, 95% CI 0.001-0.196, P < .01). UWF-OCT's complete measurability was limited in large tumors (12.5%, 1/8). CONCLUSIONS: UWF-OCT provides precise, noninvasive, single-scan assessment of small-to-medium choroidal tumors with detailed structural visualization. It may be particularly useful for dome-shaped tumors, while multimodal imaging with US and MRI remains optimal for complex morphologies. Overall, UWF-OCT represents a valuable tool for diagnosis and treatment planning, with potential utility for longitudinal follow-up in choroidal tumor management.

Humans↗

PubMed2026

Breaking the Debilitating Cycle: Pathophysiology, Assessment, and Multimodal Intervention of Secondary Debilitation After Hip Fracture in Older Adults-A Narrative Review.

Hip fractures pose a serious threat to the quality of life among older adults and impose a heavy burden on both society and families. Although current surgical techniques for hip fractures have become increasingly refined, postoperative quality of life and overall function in older adult populations often steeply decline. This decline is marked by "secondary debilitation," characterized by exacerbated sarcopenia, functional impairment, and physiological reserve depletion-a process that becomes a risk factor for recurrent fractures, creating a "vicious cycle" with hip fractures. This article provides a comprehensive overview of the pathophysiological mechanisms underlying "secondary debilitation," discusses the clinical application of risk assessment tools, and presents a phased, stepwise intervention strategy aimed at interrupting the "vicious cycle." The strategy includes early rapid rehabilitation, nutritional support, and prevention of complications; a mid-phase multimodal approach involving multidisciplinary management, comanaged wards, fracture liaison services, and systematic rehabilitation; and, finally, late-phase exploration of emerging pharmacotherapies and treatment methods. This review seeks to offer an evidence-based foundation for optimizing clinical risk assessment and developing precise interventional strategies.

Humans↗

PubMed2026

Nociception-guided opioid administration within multimodal analgesia for laparoscopic endometriosis surgery: a randomized controlled trial.

Women with endometriosis are at increased risk of severe postoperative pain due to nociceptive sensitization. While multimodal analgesia reduces opioid use, the added value of objective nociception monitoring remains unclear. This study evaluated whether NOL®-guided opioid titration improves perioperative outcomes within a standardized multimodal regimen. In this prospective, randomized, single-blinded trial, premenopausal women undergoing laparoscopic surgery for suspected endometriosis or adenomyosis were assigned to NOL®-guided analgesia or standard care based on clinical assessment. All patients received a standardized multimodal protocol. The primary outcome was total perioperative opioid consumption. Secondary outcomes included postoperative pain scores (NRS) and PACU length of stay. Exploratory analyses assessed the association between preoperative pain (Mankoski Pain Scale, MPS) and postoperative outcomes. A total of 111 patients were analyzed (NOL®: n = 54; control: n = 57). Total perioperative opioid consumption did not differ significantly between groups (adjusted mean difference = 14 μg for Fentanyl and 52 μg for Remifentanil; p = 0.8). Surgery duration was an independent predictor of opioid use (p < 0.001) and PACU length of stay (p = 0.01), whereas treatment group had no significant effect. Postoperative pain scores were comparable between groups at all time points. NOL®-derived metrics were not associated with opioid consumption or pain. Higher preoperative MPS scores independently predicted higher pain scores in the late PACU phase. NOL®-guided opioid titration did not reduce perioperative opioid consumption or improve early postoperative outcomes compared with standard multimodal analgesia in women undergoing laparoscopic surgery for endometriosis.

Humans↗

PubMed2026

Multimodal intervention benefits: Responder analysis of J-MINT PRIME Kanagawa trial.

INTRODUCTION: The J-MINT PRIME Kanagawa trial was an 18-month multimodal intervention (incorporating exercise, nutrition, and metabolic management) for dementia prevention. Because the primary analysis showed no significant benefits, we performed an exploratory responder analysis to identify responsive subpopulations. METHODS: We analyzed the Full Analysis Set comprising 188 participants. Classification and regression tree (CART) analysis, applied to the intervention arm, identified baseline predictors of cognitive improvement. These rules were then applied to the entire cohort to evaluate treatment effects on the Mini-Mental State Examination (MMSE) using fully adjusted mixed-effects models for repeated measures (MMRM). RESULTS: CART identified a "Target Group" (N = 108) characterized by baseline profiles such as an MMSE score < 28 or specific metabolic ranges (e.g., LDL-C < 135 mg/dL). Within this target group, the intervention significantly preserved MMSE trajectories compared with the control group (group × time interaction, P = 0.022). In contrast, the Non-Target Group (N = 80), consisting of high-functioning individuals (MMSE ≥ 28), exhibited no significant group × time interaction. DISCUSSION: Multimodal interventions may effectively preserve global cognition in older adults with sub-threshold cognitive decline. Careful targeting of appropriate populations, while considering potential longitudinal measurement artifacts (e.g., practice effects), is essential. These findings provide a hypothesis-generating framework that warrants external validation in future prevention trials.

Humans↗

PubMed2026

Efficacy of a multimodal conservative rehabilitation program for bladder control in individuals with incomplete spinal cord injury: A randomized controlled trial.

ObjectiveThe study aimed to evaluate the efficacy of a multimodal intervention on urodynamic outcomes, urinary incontinence severity, and pelvic floor muscle strength in individuals with overactive bladder after incomplete spinal cord injury.MethodsA single-blind randomized controlled trial was conducted on 74 male participants diagnosed with overactive bladder and incomplete spinal cord injury. Participants were randomly assigned to an experimental group or a control group. Treatment was conducted for 8 weeks, three sessions per week. Outcomes were assessed at baseline, post-intervention, and the 8-week follow-up.ResultsThe experimental group showed significantly greater improvements in the measured outcomes compared with the control group (P < 0.001).ConclusionIncorporating a multimodal regimen demonstrated significant efficacy in enhancing bladder capacity, continence, and muscle function in individuals with overactive bladder due to incomplete spinal cord injury.Clinical trial registry (ID: NCT07008157).https://clinicaltrials.gov/study/NCT07008157?cond=Spinal%20Cord%20Injuries&intr=Multimodal%20Rehabilitation%20Program&viewType=Card&rank=1.

Humans↗

PubMed2026

Effects of testosterone-augmented multimodal exercise intervention in spinal cord injury: a randomized controlled trial.

CONTEXT: Spinal cord injury (SCI) leads to profound muscle atrophy, aerobic deconditioning, and metabolic dysfunction. Exercise-based interventions alone produce modest benefits. Whether testosterone can augment physiologic responses to exercise in this population remains untested. OBJECTIVE: To evaluate efficacy and safety of home-based intervention combining functional electrical stimulation-assisted leg cycling (FES-LC), arm ergometry (AE), and testosterone compared with FES-LC, AE plus placebo in adults with SCI. METHODS: This randomized, placebo-controlled, double-blind trial enrolled 84 adults (76 males and 8 females) aged 19-70 years with SCI (neurologic levels C4-T12; AIS grades A-D). Participants were randomized to multimodality intervention (home-based FES-LC, AE and intramuscular testosterone undecanoate) (n = 38) or control intervention (FES-LC, AE plus placebo) (n = 46) for 16 weeks. The primary outcome was change in aerobic capacity (peak VO2) during AE cardiopulmonary exercise testing. Secondary outcomes included lean mass, hemoglobin, cardiometabolic markers, and safety. RESULTS: Mean (SD) age was 44 (13) years and time since injury was 13.9 (13) years). Between-group changes in peak VO2 were not statistically significant. Within-group improvements were larger in multimodality (∼19% increase; 0.10 L/min; 95% CI, 0.02-0.18 L/min) compared to controls (∼6% increase; 0.06 L/min; 95% CI, -0.01-0.13). The multimodality group gained significantly more lean mass (whole-body:1.84 kg, 95% CI: 0.52-3.16, P = .007; lower extremity 0.92 kg, 95% CI: 0.38-1.45, P = .001), and anemia was corrected in a greater proportion of participants. Adverse event rates were similar between groups. CONCLUSION: A home-based multimodality intervention combining FES-LC, AE, and testosterone was safe and associated with greater improvements in lean mass and hemoglobin. Although between-group differences in aerobic capacity were not statistically significant, greater within-group increases were observed in the multimodality group. These findings may inform future studies of testosterone-augmented exercise interventions for individuals living with SCI.

Humans↗

PubMed2026

Impact of a multimodal prehabilitation program on postoperative cognitive dysfunction: a single-center randomized controlled trial.

BACKGROUND: Postoperative cognitive dysfunction (POCD) is a frequent complication after cardiac surgery. Exercise-based prehabilitation may enhance functional reserve and reduce vulnerability to perioperative cerebral insults. We hypothesized that multimodal prehabilitation reduces POCD 3 months after cardiac surgery. METHODS: This prespecified substudy of a single-center randomized controlled trial (NCT03466606) included patients aged ≥50 years undergoing elective coronary artery bypass grafting and/or valve surgery. Participants were randomized 1:1 to 4-6 weeks of multimodal prehabilitation (exercise training, nutritional support, and psychological support) or standard preoperative care. Cognitive function was assessed at baseline and 3 months postoperatively using an age- and education-adjusted neuropsychological battery. POCD was defined as performance ≥1.5 standard deviations below normative values in at least 2 cognitive tests, excluding the Mini-Mental State Examination. Logistic regression analyses were performed to evaluate factors associated with POCD. RESULTS: Of 160 participants screened from the parent trial, 134 met eligibility criteria for the substudy and were randomized; 116 completed 3-month follow-up (prehabilitation n = 53; control n = 63). POCD occurred in 29 patients (25%), including 15/53 (28%) in the prehabilitation group and 14/63 (22%) in controls (odds ratio [OR] 1.37, 95% confidence interval [CI] 0.54-3.50, P = 0.52). In multivariable analysis, preoperative cognitive impairment was independently associated with POCD (OR 13.28, 95% CI 4.06-43.41, P < 0.001), whereas prehabilitation was not (OR 1.09, 95% CI 0.35-3.45, P = 0.877). Higher physical activity levels at 3 months were associated with lower odds of POCD (OR 0.97, 95% CI 0.95-1.00, P = 0.047). CONCLUSIONS: In this randomized controlled trial, a 4-6-week multimodal prehabilitation program did not reduce postoperative cognitive dysfunction 3 months after cardiac surgery. Although the intervention did not achieve measurable cognitive protection, the observed association between postoperative physical activity levels and postoperative cognitive dysfunction warrants further investigation.

Humans↗