Search Results (95,944)

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

Background: Proton pump inhibitors (PPIs) are widely used to prevent acid-related complications, yet concerns persist about infectious harm. Observational studies have linked PPIs to Clostridioides difficile infection (CDI) and pneumonia whereas randomized controlled trials (RCTs) consistently show reductions in upper gastrointestinal bleeding. We therefore conducted a systematic review and meta-analysis restricted to randomized controlled trials to evaluate whether PPIs increase the risk of CDI, and to assess pneumonia and gastrointestinal bleeding to contextualize net clinical benefit. Methods: A comprehensive search of randomized controlled trials (RCTs) was conducted using several databases including PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and SCOPUS until July 2025. All published English-language RCTs that met the inclusion criteria were included. Random-effects models were utilized to calculate pooled odds ratios (ORs) with 95% confidence intervals. The risk of bias was assessed using the Cochrane Risk of Bias 2.0 Tool, and heterogeneity was quantified using I² statistics. Analysis was performed using STATA version 18 and RevMan 5.3. Results: Across eight RCTs (n = 30,019), PPIs did not increase C. difficile infection versus placebo (OR 1.29, 95% CI 0.82–2.02; p = 0.27; I² = 16%) with leave-one-out (LOO) analyses showing stable estimates. In six trials reporting pneumonia, there was no significant difference between groups (OR 1.00, 95% CI 0.92–1.09; p = 0.99; I² = 0%). For clinically important upper GI bleeding (seven trials), PPIs were associated with a statistically significant lower risk when compared to placebo (OR 0.51, 95% CI 0.27–0.94; p = 0.03; I² = 56%). Conclusions: Across randomized trials with follow-up ranging from 30 days to 3 years, PPI prophylaxis significantly reduced upper gastrointestinal bleeding without increasing the risk of CDI or pneumonia. These findings support the use of PPIs for prophylaxis when clinically indicated, while recognizing that larger trials are needed to better assess rare adverse events. Full article

Background/Objectives: Contralateral breast cancer (CBC) is a significant concern among breast cancer survivors, particularly in those with moderator-high penetrance germline mutations such as BRCA1, BRCA2, CHEK2, and ATM. While radiotherapy (RT) is a crucial component of breast cancer (BC) treatment, its potential role in increasing CBC risk remains unclear. This systematic review and meta-analysis aim to evaluate the incidence of radiation-induced CBC in germline mutation carriers. Methods: Following PRISMA guidelines, we conducted a comprehensive search in six electronic databases (PubMed, Scopus, Cochrane Library, ProQuest, EBSCO, and Epistemonikos) for studies published fifteen years prior, up to August 2025. We included cohort and case–control studies assessing the association between RT and CBC incidence in germline mutation carriers. A meta-analysis was performed using a random-effects model to estimate cumulative risk (CR) and rate ratios (RR). Results: Seven studies were included. The 5-year cumulative risk (CR) of contralateral breast cancer (CBC) was 0.55 for BRCA1/2, 0.89 for ATM, and 0.80 for CHEK2 carriers. At 10 years, overall CR increased to 0.65, with ATM and CHEK2 remaining high. Rate ratio (RR) analysis showed a significant risk for ATM (2.98), while overall RR indicated more than a two-fold increased CBC risk with radiotherapy (RR = 2.70 common-effect; 2.53 random-effects). Conclusions: Radiotherapy significantly increases contralateral breast cancer risk, particularly in ATM and CHEK2 carriers, emphasizing the importance of personalized genetic risk stratification in treatment decisions. Full article

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide),YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article

The COVID-19 pandemic has been associated with a substantial number of deaths, exposing many individuals to bereavement under particularly adverse circumstances, as public health restrictions often prevented individuals from engaging in customary farewell and mourning practices. In this context, the development of interventions capable of mitigating the psychological impact of grief is of critical importance. This mixed-methods study, with a predominantly qualitative design, aimed to develop and pilot-test a group intervention grounded in cognitive-narrative theory for individuals experiencing bereavement during the COVID-19 pandemic, in Portugal. Four patients aged between 18 and 65 years (M = 49.25; SD = 21.24) participated in the 6-week intervention, between July and August 2022. Quantitative data were collected using the Grief and Meaning Reconstruction Inventory, the Prolonged Grief Assessment Instrument, and the Hospital Anxiety and Depression Scale, with pre- and post-intervention comparisons. To assess the intervention process, participants completed an individual evaluation form, and a group interview was conducted at the end of the intervention. The results indicated a clinically significant reduction in feelings of emptiness and loss of meaning in most participants, with improved meaning-making related to the loss. The thematic analysis performed on the qualitative data highlighted the strengths of the intervention (e.g., adjustment to grief and sharing) and some areas for improvement (e.g., more regular feedback and group composition). Despite limitations, particularly the small sample size, the findings are promising and support further evaluation of this intervention in larger samples of individuals diagnosed with prolonged grief. Full article

Background: Vitamin B12 deficiency is a recognized but frequently under-integrated cause of global developmental delay (GDD) in infancy and early childhood. Early diagnosis is critical because neurological impairment may be partially or completely reversible with timely treatment. Objective: This narrative review aims to synthesize current evidence on the role of vitamin B12 deficiency in the diagnostic evaluation of GDD, with a focus on clinical phenotype, risk factors, biomarkers, treatment outcomes, and practical integration into contemporary diagnostic algorithms. Methods: A structured, non-systematic search of PubMed/MEDLINE, Embase, and Web of Science was performed to identify clinical studies, case series, reviews, and guideline documents addressing pediatric vitamin B12 deficiency and neurodevelopmental delay. Results: Vitamin B12 deficiency in early childhood is most commonly associated with maternal deficiency and exclusive breastfeeding without adequate supplementation. Evidence from recent clinical and observational studies indicates that vitamin B12 deficiency may present with nonspecific neurological symptoms, including developmental regression, hypotonia, and feeding difficulties. Incorporating vitamin B12 assessment—using serum vitamin B12, holotranscobalamin, methylmalonic acid, and homocysteine—into early diagnostic algorithms for GDD may facilitate timely identification of a treatable cause of neurodevelopmental impairment. The proposed diagnostic framework emphasizes early biochemical evaluation in infants with unexplained developmental delay, thereby supporting prompt treatment during a critical window of neurological reversibility. Conclusions: Targeted assessment of vitamin B12 status in children with GDD, together with evaluation of maternal status, represents a clinically relevant approach to identifying a potentially preventable and treatable cause of neurodevelopmental impairment. Integration of functional biomarkers into diagnostic pathways and the development of pediatric-specific reference standards are key priorities for future research and clinical practice. Full article

The transition to net-zero energy systems requires operationally grounded decision-making frameworks that integrate technology performance, infrastructure readiness, and policy constraints at local scale. Food transportation represents a high-emission and operationally critical component of regional energy and supply chain systems, particularly in food-producing regions. This study proposes a preliminary Local Area Energy Planning (LAEP) framework to support operational decision-making for the decarbonisation of food transportation, using Lincolnshire, UK, as a case study. The framework evaluates alternative freight transport technologies—battery electric vehicles (BEVs), hydrogen fuel cell electric vehicles (HFCEVs), battery electric road systems (BERS), and conventional internal combustion engine vehicles—across energy efficiency, CO₂ emissions, infrastructure requirements, and cost implications. Secondary data from national statistics, regional planning documents, and peer-reviewed literature are analysed using comparative quantitative and qualitative assessment methods. Results indicate that BEVs currently offer the most energy-efficient and cost-effective solution for short-haul and last-mile food logistics, achieving overall efficiencies of approximately 77–82% with zero tailpipe emissions. HFCEVs and BERS present potential long-term operational advantages for heavy-duty and long-haul freight, but remain constrained by high infrastructure investment, energy conversion losses, and system-level costs. The findings highlight the importance of phased technology adoption, renewable energy integration, and infrastructure prioritisation to enable sustainable energy operations in freight transport systems. By embedding technology comparison within a place-based planning framework, this study contributes actionable insights for local authorities, logistics operators, and policymakers seeking to support operational decision-making in sustainable energy systems. The proposed LAEP framework is transferable to other food-producing regions aiming to decarbonise freight transportation while maintaining operational efficiency. Full article

Visual attention involves the efficient allocation of processing resources across space and under conditions of visual competition. This study examined whether experience-related advantages in visual attention are expressed uniformly or selectively across attentional components. Using a modified Useful Field of View (UFOV) paradigm, four groups with distinct experiential backgrounds were compared: table tennis players (TTPs), action video game players (AVGPs), aerobic gymnastics athletes (AGAs), and non-trained college students (NCSs). Subtest 1 assessed central identification under relatively low attentional control demands. No significant group differences were observed, indicating comparable basic central identification performance across groups. Subtests 2 and 3 assessed divided attention and selective attention under interference, respectively. In Subtest 2, all experienced groups outperformed NCSs, with no differences among TTPs, AVGPs, and AGAs. In Subtest 3 under high visual competition, performance diverged; TTPs and AVGPs outperformed both AGAs and NCSs, whereas AGAs did not differ from controls. These findings indicate that experience-related advantages in visual attention are component-specific rather than global, and become most evident when tasks place stronger demands on attentional control under interference. The advantage pattern shown by TTPs under higher attentional control demands was more compatible with visually demanding experience than with physical training alone. No significant interactions with eccentricity were observed, suggesting consistent group differences across peripheral distances. Full article

Background/Objectives: Breast cancer screening such as mammography supports earlier detection, but variability in interpretation can still lead to missed cancers and avoidable follow-up testing. We evaluated ChatGPT-5.2 Thinking (OpenAI) as a stand-alone model for examination-level malignancy classification on standard bilateral mammography views in a biopsy-referred cohort, compared with breast radiologists, and assessed laterality performance. Methods: We conducted a retrospective, multicenter diagnostic-accuracy study across breast imaging centers in Saudi Arabia. From an upstream screened cohort (n = 1225), we constructed a biopsy-referred test set of 100 mammography examinations (four 2D views per exam: bilateral CC and MLO; 400 images), including 61 biopsy-confirmed malignancies and 39 biopsy-negative controls, with pathology as the reference standard. Radiologists were blinded to pathology and AI outputs and assigned BI-RADS (0–5) and suspected laterality. ChatGPT-5.2 interpreted the same de-identified views using a BI-RADS-guided prompt to generate BI-RADS and laterality. The sensitivity, specificity, accuracy, and laterality concordance were then estimated. Results: ChatGPT-5.2 had higher sensitivity than radiologists (95.08% vs. 81.97%) but markedly lower specificity (10.26% vs. 56.41%), resulting in lower overall accuracy (62.00% vs. 72.00%). The AI produced 58 true positives, 35 false positives, and 3 false negatives, while radiologists produced 50 true positives, 17 false positives, and 11 false negatives. Laterality accuracy among malignant examinations was 60.66%. Conclusions: In this pathology-anchored, biopsy-referred evaluation, ChatGPT-5.2 identified more cancers but generated substantially more false-positive classifications and showed only moderate breast-side localization. These findings support use as a concurrent aid or prioritization tool rather than a stand-alone reader and motivate efforts to improve specificity and laterality before prospective validation. Full article

Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH₃-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination ($R_{adj}^2$) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies. Full article

Traditionally, repairing coated substrates requires completely removing damaged, wear-resistant layers before recoating. This process leads to high costs, extended downtime, and material waste. Flexible brazing tapes, which are composed of alloy powder and an organic binder, offer an alternative to full coating removal for targeted repairs. Despite this, the process of vacuum brazing these tapes may lead to the formation of defects, including pores caused by trapped gases or residual binder, which compromise coating durability and corrosion resistance. This study focuses on the utilization of laser remelting as a method for post-processing nickel- and iron-based mixed alloy brazing tapes, with the aim of improving the integrity of the coating. Surface quality was assessed via microscopy and microhardness testing by systematically varying laser power, scanning speed, and hatch distance. Among the parameters studied, the most suitable laser parameter combination was found to be 350 W laser power, 250 mm/s scanning speed, and a hatch distance of 0.02 mm. These parameters yielded crack- and pore-free coatings with a remelting depth of 160.3 ± 17.2 µm and a microhardness of 701 ± 23 HV1, which is an 85% increase over as-brazed samples. Wear testing revealed a reduced coefficient of friction, and electrochemical corrosion tests showed lower corrosion current density and enhanced repassivation behavior in remelted coatings. These improvements demonstrate that laser remelting significantly enhances the microstructure, hardness, wear resistance, and corrosion performance of brazed coatings, providing an effective method for localized repair while minimizing material consumption and processing duration. Full article

Background: Serum neurofilament light chain (sNfL) is an established biomarker of neuroaxonal damage in multiple sclerosis (MS). Despite its prognostic utility, patient awareness of its clinical application remains poorly characterized. The objective of this study was to assess the acceptance of sNfL monitoring among patients with early-stage relapsing–remitting MS (RRMS) and identify factors predicting their willingness to adopt this tool. Methods: This non-interventional, cross-sectional study was conducted across 16 neuroimmunology clinics. We included RRMS patients with a disease duration of ≤3 years receiving disease-modifying therapy. Acceptance was assessed following a standardized educational tutorial. Multivariable logistic regression was employed to identify predictors of patient acceptance. Results: The study included 144 patients (mean age 37.6 [SD 10.3] years, 69.4% female). Only 19.4% (n = 28) had prior awareness of sNfL. However, after the tutorial, 84.0% (n = 121) expressed willingness to adopt sNfL testing. Furthermore, 62.5% (n = 90) indicated that normal sNfL levels would provide emotional reassurance between clinical visits. Patients willing to undergo testing showed higher disease knowledge, less treatment regret, and better physical quality of life and cognitive performance. In the multivariable analysis, higher disease knowledge (OR = 1.52, 95%CI 1.16–1.99; p = 0.002) and lower symptom burden (OR = 0.96, 95%CI 0.93–0.99; p = 0.038) were associated with greater acceptance. Conclusions: Patients demonstrate high receptivity to sNfL monitoring when provided with adequate clinical context. Because disease knowledge is a primary driver of acceptance, personalized educational initiatives may be a complementary strategy to facilitate the integration of precision biomarkers into MS management. Full article

Comparative evaluations of different serological methods for the diagnosis of toxoplasmosis in pregnant women remain limited, and the performance of indirect immunofluorescence assay (IFA), modified agglutination test (MAT), and chemiluminescent microparticle immunoassay (CMIA) has not been previously assessed simultaneously in this population. This study aimed to compare the performance of these three serological methods for the detection of Toxoplasma gondii antibodies in pregnant women. A total of 469 serum samples were collected from pregnant women receiving prenatal care through the Brazilian public healthcare system. Samples were tested using IFA, MAT, and CMIA for the detection of IgG and IgM antibodies. Statistical analyses included McNemar’s χ² test, Kappa agreement, and Pearson and Spearman correlation coefficients. IFA and MAT showed higher IgG seropositivity rates (53.1% and 51.2%, respectively) compared to CMIA (46.7% and 48.6%). Agreement between CMIA and IFA was moderate for IgG (Kappa = 0.51) and very strong for IgM (Kappa = 0.89). Pearson’s correlation for IgG between CMIA and IFA was moderate (r = 0.678), while Spearman’s correlation for IgM was weak. IFA and MAT demonstrated greater sensitivity for IgG detection than CMIA, while CMIA and IFA performed similarly for IgM. Conventional methods may complement automated systems to improve diagnostic accuracy in prenatal screening. Full article

Background: Traditional functional mobility assessments often fail to detect subclinical postural decline in active aging populations. This study introduces the Head–Sternum Dissociation Index as a novel digital biomarker to identify latent sensorimotor deficits before macroscopic balance failure occurs. Methods: Ninety-four participants (Young, Middle-Aged Civil, Middle-Aged Dancers, and Older Adults) performed instrumented limits of stability tasks, specifically functional and lateral reach tests, utilizing a three-sensor inertial measurement unit configuration. Postural strategies were quantified via the Head–Sternum Dissociation Index and the peak ratio of corrective micro-movements, validating the sensor output against a gold-standard force platform. Results: A significant kinematic breakpoint in postural control was identified at age 55 (p < 0.001). However, Middle-Aged Civilians exhibited early kinematic divergence despite maintaining normal Timed Up and Go test performance. Receiver operating characteristic analysis revealed distinct, sex-specific physiological limits: aging males predominantly adopted a rigid “Stiffness” strategy (peak ratio ≤ 1.15, head–sternum dissociation threshold > 0.63°), while females utilized a broader, more permissive “Continuous” strategy (head–sternum dissociation threshold > 0.31°). Notably, recreational rhythmic training (dance) completely neutralized this age-related decay, with middle-aged dancers maintaining highly efficient, youthful stabilization profiles (Cohen’s d = 2.20). Conclusions: The Head–Sternum Dissociation Index, combined with relative corrective frequency, successfully phenotypes early sensorimotor erosion. These findings advocate for the integration of sex-specific kinematic screening into primary care, allowing clinicians to prescribe targeted interventions well before clinical fall risk manifests. Full article

Background: Uropathogenic Escherichia coli (UPEC) is the primary etiological agent of urinary tract infections (UTIs) worldwide. The emergence of strains combining high virulence with multidrug resistance (MDR) poses a significant challenge to public health. This study aimed to characterize the phylogenetic distribution, virulence profiles, and antimicrobial susceptibility of UPEC isolates recovered from patients in the metropolitan area of Buenos Aires (AMBA), Argentina. Methodology: Phylogenetic groups, the ST131 lineage, and virulence-associated genes were identified using PCR-based assays. Antimicrobial susceptibility testing (AST) was performed using automated methods and extended-spectrum beta-lactamase (ESBL) production was confirmed using the double-disk synergy test. Colistin (COL) resistance was evaluated by Colistin Drop Test and PCR screening for the mcr-1 (mobile colistin resistance gene 1). Biofilm formation was detected by the Tissue Culture Plate (TCP) method, whereas phenotypic virulence factors (VF) were assessed with Congo Red agar, hemagglutination, and hemolysis assays. Results: Phylogenetic groups B2 (43.8%) and D (26.7%), typically associated with extraintestinal infections, were the most frequent. The high-risk clone B2-ST131 was detected in 6.7% of isolates. Biofilm production was observed in 92.4% of the isolates, with curli fimbriae (87.6%) being the most frequently expressed VF. The highest resistance rates were observed for ampicillin (62.1%), ampicillin-sulbactam (39.8%), and trimethoprim-sulfamethoxazole (25.2%). Interestingly, 3.8% of isolates exhibited colistin resistance, despite the absence of the mcr-1 gene. Conclusions: This study highlights the detection of MDR-UPEC isolates that showed strong resistance to fluoroquinolones and were ESBL producers with high virulence in Argentina, justifying future research encompassing genomic and epidemiological monitoring of local UPEC, which is essential for managing infections and developing new therapeutic and preventive measures. Full article