SEARCH · Search PubMed

Results for “Imaging, Three-Dimensional”

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.

244 records · Page 2Linked to original sources

PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗

PubMed2026

Interface-dependent V. parahaemolyticus biofilm under varying temperatures, media, and oxygen conditions: implications for seafood safety.

Vibrio parahaemolyticus biofilms play a critical role in pathogen persistence in marine and seafood-processing environments, where oxygen availability, temperature, and surface interfaces vary widely. This study investigated biofilm development by three strains on partially submerged stainless-steel coupons under gas-liquid-wall (GLW) and fully submerged (SM) interfaces. Viable cell counts (log₁₀CFU/cm2) along with normalized protein concentration per viable cell (nProt) and normalized polysaccharide concentration per viable cell (nPol) were measured, under aerobic and anaerobic conditions across a temperature range of 15-30 °C, using tryptic soy broth with 3% NaCl (TSB) and seawater-based medium (SW). GLW biofilms consistently exhibited higher cell counts (6.4-7.3 log₁₀CFU/cm2) compared to SM biofilms (5.9-6.3 log₁₀CFU/cm2), suggesting that enhanced oxygen diffusion promotes bacterial proliferation. Conversely, SM biofilms exhibited significantly higher nProt and nPol levels (p < 0.001), indicating increased production of the extracellular polymeric substance (EPS) matrix under low-oxygen, high-nutrient conditions. Microscopy and three-dimensional surface plot analyses revealed relatively uniform biofilm layers at the GLW interface, whereas SM biofilms formed heterogeneous, tower-like structures. EPS production was further influenced by medium composition, oxygen, and temperature. SM biofilms grown in SW exhibited significantly higher nProt and nPol than those in TSB under aerobic conditions (p < 0.001), indicating enhanced matrix stabilization. Under anaerobic conditions at 15 °C, nProt and nPol were higher, whereas under aerobic conditions, peak nProt and nPol occurred at elevated temperatures. These findings highlight a trade-off between bacterial growth and matrix production and provide insight into biofilm adaptation and persistence in seafood-processing environments. These insights may help develop improved biofilm control and seafood safety management.

Biofilms↗

PubMed2026

Effects of strength and balance training on the structure of the aging brain.

BACKGROUND: While it is established that motor training induces structural changes in the brains of young adults, structural adaptations in aging brains are less studied. METHODS: This randomized controlled study investigated the impact of long-term strength and balance training on the structural plasticity in 60 elderly adults (64 - 82 years old, 70.6 ± 4.7) using multi-modal neuroimaging. We compared the effects of three months of strength training to balance training of the same duration and to a passive control group. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to assess grey matter (GM) and white matter (WM) plasticity. White matter tract integrity (WMTI) modelling was employed to explore the microstructural underpinnings of white matter alterations. RESULTS: We found that strength training was associated with changes in diffusion metrics consistent with white matter microstructural remodeling, specifically increased extra-axonal axial diffusivity in the bilateral inferior fronto-occipital and longitudinal fasciculi. Additionally, both balance and strength training mitigated reductions in axonal water fraction in the splenium of the corpus callosum and the right posterior corona radiata observed in the control group. CONCLUSION: These results underscore the potential relevance of strength and balance training to induce beneficial neural plasticity by counteracting aging-related demyelination in the corpus callosum and highlight the specific role of strength training in facilitating white matter reorganization in key transmission fiber pathways.

Humans↗

PubMed2026

Spatially confined electrochemical strategy with DNA-assembled nanogaps for SNP detection.

Accurate detection of low-abundance single nucleotide polymorphisms (SNPs) against a large excess of homologous wild-type sequences requires both selective molecular recognition and effective transduction of small sequence differences into measurable signals. Here, we report a spatially confined electrochemical strategy that couples sequence-selective recognition with size-dependent mass-transport gating. DNA-hybridization-driven self-assembly of gold nanoparticles (AuNPs) forms a three-dimensional self-assembled electrode (3D-SAE) with a DNA-defined interparticle architecture. Competitive probes (SP/WP) convert single-base recognition into distinct molecular-size states: the SNP-associated pathway preferentially triggers a hybridization chain reaction (HCR), generating bulky AuNP-anchored HCR/methylene blue complexes (Au@HCR/MB) with reduced electrochemical accessibility through the porous 3D-SAE, whereas the wild-type pathway does not trigger HCR and maintains a high-current response from more readily accessible MB-containing species. Thus, sequence recognition is translated into a molecular-size difference and subsequently into an electrochemical signal through differential mass transport. Under buffer conditions, the platform achieved a statistically estimated detection limit of ∼0.47 fM and a quantitative range of 1 fM-100 pM. It discriminated a 0.1% mutant abundance in a fragmented genomic-DNA background. The downstream signal-transduction chemistry is enzyme-free and isothermal. This work establishes a mechanistical recognition-size-conversion-mass-transport-gating architecture for electrochemical nucleic acid analysis.

Polymorphism, Single Nucleotide↗

PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗

PubMed2026

Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs.

BACKGROUND: Most European population mammography screening programs rely on double reading with arbitration, a model that delivers mortality benefit but is increasingly challenged by radiologist workload, variable specificity, and interval cancers. Artificial intelligence (AI) is being evaluated to support or optimize these established European screening pathways. PURPOSE: To synthesize prospective or program-embedded evaluations of AI conducted within European-style population screening programs and to estimate exploratory program-level absolute risk differences (RDs) per 1000 examinations for cancer detection rate (CDR) and recall. MATERIALS AND METHODS: We performed a prespecified, focused evidence synthesis of three large studies embedded within routine population screening programs operating under European-relevant workflows: MASAI (randomized AI-supported risk triage within a national program), ScreenTrustCAD (prospective paired-reader evaluation with AI as an independent reader in a double-reading framework), and PRAIM (nationwide decision-referral implementation). Outcomes were harmonized as AI-control RDs per 1000 examinations. Random-effects pooling used Hartung-Knapp-Sidik-Jonkman models. For the paired-reader design, sensitivity analyses applied a Kish effective sample-size approach across plausible within-examination correlations (ρ = 0.3-0.8). Positive predictive value (PPV) and workflow/time outcomes were summarized descriptively. RESULTS: Across 597,419 examinations, the pooled CDR RD was +0.9 per 1000 (95% CI -0.0 to +1.8; I2 ≈ 12%), consistent with a modest directional increase with borderline statistical uncertainty. The pooled recall RD was -0.6 per 1000 (95% CI -3.1 to +2.1; I2 ≈ 41-43%), indicating no consistent recall increase across screening programs. Where reported, PPV was higher with AI-supported screening. Efficiency signals included 44.3% fewer total readings in MASAI and shorter reading times for AI-normal examinations in PRAIM; in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed. CONCLUSION: In European population screening programs characterized by double reading and arbitration, prospective program-embedded evidence suggests that AI integration may yield a small absolute increase in cancer detection (≈1/1000) without a consistent increase in recall, alongside improved PPV and efficiency signals. These findings suggestAI primarily as a complementary reader within European screening workflows, with implementation requiring explicit quality assurance and monitoring of interval cancers and stage distribution.

Humans↗

PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

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

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

Quantitative susceptibility mapping in neurodegenerative diseases: An umbrella review of iron-related biomarkers and mechanisms.

Pathological iron accumulation is a common pathophysiological hallmark across multiple neurodegenerative diseases (NDDs), motivating the need for accurate, non-invasive quantification methods. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique that enables in vivo measurement of tissue magnetic susceptibility (χ), providing a sensitive proxy for iron content. This umbrella review systematically evaluates the diagnostic accuracy, clinical correlations, and distinct iron distribution patterns of QSM in major NDDs, such as Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and atypical Parkinsonism. We included 15 (13/15 were rated Low or Critically Low on AMSTAR 2) systematic reviews and meta-analyses (through July 15, 2026); however, the findings should be interpreted cautiously because of heterogeneity and the low methodological quality. A Corrected Covered Area (CCA) analysis demonstrated only slight overlap of primary studies across the included reviews (CCA = 5.42%). Collectively, the evidence indicates that QSM provides comparable or higher diagnostic sensitivity and reliability than conventional R2* and SWI techniques, particularly for deep gray matter structures. The findings support significant iron overload in the substantia nigra, particularly in the pars compacta, as a robust biomarker for PD that correlates with motor severity and disease duration. Furthermore, regional iron profiling in the basal ganglia is critical for differential diagnosis; specifically, elevated χ in the putamen and globus pallidus effectively distinguishes multiple system atrophy and progressive supranuclear palsy from idiopathic PD. Distinctively, AD and ALS exhibit specific χ alterations in the thalamus, motor cortex, and hippocampus, reflecting divergent iron-related pathophysiological mechanisms, which correlate with cognitive impairment and upper motor neuron signs. Overall, QSM shows diagnostic promise and offers mechanistic insights into iron-related neurodegenerative processes.

Humans↗

PubMed2026

Single-slice Functional Lung MRI During Metronome-Paced Tachypnea Detects Changes in Regional Ventilation Dynamics After a Single Dose of Dual Bronchodilator Treatment in COPD.

Dual long-acting bronchodilators are a standard treatment in chronic obstructive pulmonary disease (COPD), aimed at alleviating dyspnea, improving exercise tolerance, and preventing exacerbations. Metronome-paced tachypnea (MPT) offers a feasible alternative to exercise testing for the evaluation of dynamic hyperinflation (DH) in COPD. Because MPT can be performed during MRI, its combination with single-slice phase-resolved functional lung (PREFUL) MRI provides a promising approach to investigate changes in regional ventilation dynamics induced by DH. This approach was evaluated in a randomized, investigator-blinded, placebo-controlled, single-dose (SD) crossover trial with a two-week extension of once daily dual bronchodilator medication, in which patients with stable COPD underwent PREFUL MRI during resting tidal breathing (RTB) and during MPT. During RTB, no significant improvements in MRI-derived parameters were observed after SD treatment compared with placebo. During MPT, however, regional ventilation, flow-volume loop correlation, its defect percentage, and end-expiratory lung area improved significantly after SD treatment compared to the placebo scan (all p < 0.02). After multi-dose treatment, five out of six measured parameters improved during MPT, when compared to the baseline scan without bronchodilator treatment (all p < 0.03). In contrast to RTB, PREFUL MRI during MPT was able to detect changes in COPD patients already after SD treatment. The combination of MPT and PREFUL MRI represents a promising method to evaluate the effects of dual bronchodilators on regional ventilation dynamics and hyperinflation.

Humans↗

PubMed2026

Cine-derived mitral annular relaxation velocity for detection of preclinical left ventricular diastolic dysfunction.

OBJECTIVES: Imaging diastolic dysfunction in pre-clinical heart failure (HF) is challenging. We evaluated a novel cardiac MRI (CMR) biomarker, CMR e-prime (CMR-MARV), in patients at risk of HF. METHODS: In this substudy of the PARABLE trial (NCT04687111), 236 patients (71.6 ± 7.7 years, 61.6% male) fulfilling trial-defined ALVDD citeria underwent CMR with measurement of mitral annular relaxation velocity (CMR-MARV) at four mitral annular anchor points. Diastolic strain rates from FT were also assessed. Twenty-five age- and sex-matched controls were included (73.8 ± 3.1 years, 52% male). Group differences were tested with t-tests, diagnostic accuracy with ROC analysis, and predictors of diastolic dysfunction with adjusted logistic regression. RESULTS: Compared with controls, patients had significantly higher indexed maximal left atrial volume (LAVimax), LV end-diastolic and end-systolic volumes, and LV mass (all p < 0.001). Of FT variables, only peak diastolic longitudinal velocity differed between groups (p < 0.001). In multivariate models, CMR-MARV correlated with radial, circumferential, and longitudinal diastolic strain rates, radial and longitudinal diastolic velocities (all p < 0.001), echocardiographic e' (r = 0.20, p = 0.007), LV mass (r = -0.18, p = 0.008), LAVimax (r = -0.18, p = 0.008), and NT-proBNP (r = -0.30, p < 0.0001). LAVimax and CMR-MARV were strongly independently associated with ALVDD (AUC 0.89 and 0.76, respectively; p < 0.0001). A combined model (LAVimax + CMR-MARV) achieved excellent discrimination (AUC 0.91, 95% CI 0.86-0.97, p < 0.0001). Independent predictors included LAVimax, CMR-MARV, and peak diastolic longitudinal velocity (all p < 0.001). CONCLUSION: CMR-MARV provides a simple cine-derived measure of longitudinal relaxation that correlates with established structural and biochemical markers of diastolic burden. Within an at-risk population, it offers incremental functional information beyond conventional parameters and may support multiparametric CMR phenotyping of preclinical diastolic dysfunction.

Aged↗

PubMed2026

High-frequency contralesional dorsal premotor cortex and low-frequency contralesional primary motor cortex rTMS in subacute stroke with severe upper limb impairment: comparable motor outcomes and differential regional degree centrality changes.

BACKGROUND: The contralesional dorsal premotor cortex has been proposed as a potential neuromodulatory target for patients with severe upper limb impairment due to subacute ischemic stroke. This proof-of-concept study aimed to compare behavioral outcomes and resting-state neuroimaging findings between high-frequency repetitive transcranial magnetic stimulation (rTMS) over the contralesional dorsal premotor cortex and guideline-supported low-frequency stimulation over the contralesional primary motor cortex. METHODS: In this randomized trial, 46 patients with severe upper limb impairment in the subacute stage after ischemic stroke were randomly assigned to receive either high-frequency rTMS over the contralesional dorsal premotor cortex or low-frequency rTMS over the contralesional primary motor cortex. Low-frequency stimulation over the contralesional primary motor cortex served as an evidence-supported active comparator for poststroke upper limb motor recovery. Stimulation was administered five times per week for two weeks using magnetic resonance imaging-guided neuronavigation. All participants received concurrent standard rehabilitation therapy. The primary outcome was the Fugl-Meyer Assessment for Upper Extremity. Secondary outcomes included the Arm Subscore of the Motricity Index, the Hong Kong version of the Functional Test for the Hemiplegic Upper Extremity, the Modified Barthel Index, and resting-state functional magnetic resonance imaging-derived degree centrality. RESULTS: Both groups showed significant improvements in the primary and secondary behavioral measures (p < 0.01), with no significant between-group differences in the magnitude of change (p > 0.05). In neuroimaging analyses, patients receiving high-frequency rTMS over the contralesional dorsal premotor cortex showed significantly greater degree centrality changes in the ipsilesional middle occipital gyrus, contralesional medial superior frontal gyrus, and contralesional middle frontal gyrus than those receiving low-frequency rTMS over the contralesional primary motor cortex (p < 0.05). Within the high-frequency stimulation group, degree centrality changes in the ipsilesional middle occipital gyrus were positively correlated with improvements in the Fugl-Meyer Assessment for Upper Extremity (r = 0.619, false discovery rate-corrected p = 0.018). CONCLUSIONS: High-frequency rTMS over the contralesional dorsal premotor cortex produced behavioral improvements comparable to guideline-supported low-frequency rTMS over the contralesional primary motor cortex, without establishing superiority or formal non-inferiority. Exploratory neuroimaging analyses showed greater degree centrality changes in the ipsilesional middle occipital gyrus after high-frequency premotor stimulation, and these changes correlated with upper-limb motor improvement. These findings support further investigation of contralesional dorsal premotor cortex-targeted high-frequency rTMS for severe subacute post-stroke upper limb impairment. REGISTRATION: URL: http://www.chictr.org.cn; Unique identifier: ChiCTR2000038049.

Humans↗

PubMed2026

Comprehensive quality profiling and comparative metabolic characterization of seven dominant fresh-eating Chinese olive (Canarium album Lour.) cultivars in Southern China.

Fresh-eating Chinese olive (Canarium album Lour.) is a subtropical fruit endemic to southern China with considerable commercial value, yet systematic quality characterization of dominant cultivars remains scarce. This study established a multi-dimensional quality dataset for seven dominant cultivars from Fujian and Guangdong provinces, integrating nutritional components, soluble sugars, organic acids, mineral elements, volatile profiles, and non-targeted metabolomics. Significant cultivar-specific differences were observed across all evaluated dimensions: "Lingfeng" exhibited a sugar-dominant low-acid profile, whereas "Sanleng" showed elevated phenolic constituents accumulation. Volatile profiling identified terpenoid-based candidate discriminatory biomarkers, and metabolomic analysis revealed phenylpropanoid biosynthesis, tryptophan metabolism, and starch and sucrose metabolism as the most variable pathways. Correlations between untargeted profiling and targeted absolute quantification validated untargeted result reliability and revealed their complementarity in nutritional evaluation. These findings provide baseline data for FECO germplasm evaluation and targeted industrial utilization.

China↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Orbital involvement in sickle cell disease: A systematic review.

Orbital involvement in sickle cell disease (SCD) is rare but potentially vision-threatening and is often misdiagnosed due to overlap with infectious orbital disease. We conducted a systematic review of case reports and series describing orbital complications in patients with confirmed SCD, following PRISMA and MOOSE guidelines. Across 53 studies, 76 cases were identified. Patients were predominantly male (77.6%), with an average age of 13.2 years. Orbital disease was the initial SCD manifestation in 6.6%. Presentations included periorbital edema in all, proptosis in 64.1%, restricted ocular motility in 56.5%, reduced visual acuity in 28.1%, and bilateral involvement in 38.2%. Laboratory findings commonly included leukocytosis (73%) and raised inflammatory markers (86.7%). Radiologically, orbital subperiosteal hematoma were observed in 70%, combined orbital bone infarction and hematoma in 38.2%, and orbital bone infarction alone in 19.7%. Magnetic resonance imaging is critical for accurate diagnosis. Intracranial hemorrhage was present in 9.2%. Less frequent manifestations included orbital apex syndrome, lacrimal gland disease, and nonspecific soft tissue swelling. Management was primarily conservative (82.9%), and surgery was reserved for vision-threatening or intracranial complications. Complete recovery was achieved in 93.1% of cases. While severe vision-threatening complications are uncommon, early recognition remains critical to optimising outcomes in sickle cell orbitopathy.

Humans↗

PubMed2026

Mitigating pH-induced instability in deruxtecan-based ADCs: an onboard-mixing icIEF approach for robust charge heterogeneity characterization.

Accurate charge variant analysis of antibody-drug conjugates (ADCs) is essential for understanding product heterogeneity and ensuring quality control. However, Deruxtecan (DXd)-based ADCs present a unique analytical challenge due to the intrinsic instability of the payload, where the lactone ring readily undergoes hydrolysis under alkaline conditions, resulting in time-dependent shifts in charge distribution during imaged capillary isoelectric focusing (icIEF). In this study, we describe the development of an onboard-mixing icIEF method designed to minimize pH-induced degradation during sample preparation. By separating ADC samples from carrier ampholytes (CAs) prior to injection and enabling real-time mixing within the instrument, this approach effectively suppresses premature lactone ring opening and stabilizes charge variant profiles. Comparative studies between conventional premixing and onboard-mixing approach demonstrated that the latter significantly enhances reproducibility, particularly for acidic variants that are highly sensitive to structural conversion. Comprehensive method validation confirmed excellent precision, linearity, and sensitivity, with consistent performance across run-to-run and intra-day analyses. The results underscore the importance of controlling microenvironmental pH exposure in the analysis of chemically instable ADCs. The proposed onboard-mixing strategy provides a robust and efficient solution for icIEF-based characterization, reducing analytical artifacts while simplifying method development. This approach is broadly applicable to ADCs and other biotherapeutics containing pH-sensitive functional groups.

Hydrogen-Ion Concentration↗