Search Results (1,839)

Background/Objectives: Streptococcus parasuis is an under-recognized member of the Streptococcus suis complex that can be misidentified by routine diagnostic methods. Human clinical isolates remain sparsely characterized. This study investigated the antimicrobial phenotype, genomic features, population position, and larval pathogenicity of a human pleural-fluid isolate. Methods: Strain HUGSPH was recovered during the care of a patient with pneumonia. Hybrid whole-genome sequencing (WGS), average nucleotide identity analysis, multilocus sequence typing (MLST), core-genome phylogenetics, comparative pan-genomics, and an exploratory host-origin association analysis were performed. Minimum inhibitory concentrations (MICs) were determined using the VITEK 2 Compact system with an AST-ST03 card, and virulence was evaluated in Galleria mellonella larvae. Results: Genome-based analysis confirmed HUGSPH as S. parasuis and assigned it to the novel sequence type (ST) 181. The isolate clustered within a predominantly human-origin phylogenetic clade. Because species-specific clinical breakpoints are unavailable, the MICs were interpreted provisionally using explicitly stated surrogate Clinical and Laboratory Standards Institute (CLSI) criteria; erythromycin, clindamycin, and levofloxacin were categorized as resistant under those criteria. Several predicted antimicrobial-resistance-associated genes were detected. In a restricted comparison of seven human-origin and nine swine-origin genomes, eight genes were present in all included human-origin isolates and absent from all included swine-origin isolates, whereas metQ, metP, and metN showed the reciprocal pattern. HUGSPH caused dose-dependent larval mortality, reaching 100% at 10⁷ colony-forming units (CFU) by 96 h. Conclusions: HUGSPH expands the genomic record of human clinical S. parasuis and highlights the diagnostic and surveillance relevance of antimicrobial resistance in this species. The susceptibility categories, source-associated genes, and larval phenotype require validation by reference susceptibility testing, broader phylogenetically balanced collections, comparative strains, and mammalian models. Full article

Background and Objectives: Codon usage bias (CUB), the non-uniform usage of synonymous codons, is prevalent in plant chloroplast genomes and plays important roles in gene expression and genome evolution. However, systematic comparisons of CUB patterns across genera in the Cucurbitaceae family remain limited. This study aimed to characterize CUB patterns and identify their driving forces in the chloroplast genomes of six Cucurbitaceae species. Methods: We analyzed the complete chloroplast genomes of six Cucurbitaceae species, watermelon (Citrullus lanatus), melon (Cucumis melo), cucumber (Cucumis sativus), pumpkin (Cucurbita moschata), wax gourd (Benincasa hispida), and bitter gourd (Momordica charantia). CUB patterns were assessed using ENC-plot, neutrality plot, and PR2-plot analyses, all implemented through a custom, reproducible Python-based workflow. Results: The overall codon usage bias was weak across all six species, with a clear preference for A/U-ending codons. Candidate preferred codons (hereafter referred to as optimal codons for brevity) identified independently within each species were largely shared across the six species, although species-specific codons were also detected. Combined ENC-plot, neutrality plot, and PR2-plot analyses suggest that natural selection, rather than mutation pressure, is the dominant force shaping CUB in these genomes, although these approaches provide indirect evidence. Conclusions: These findings provide a reference for codon optimization of exogenous genes in chloroplast genetic engineering of Cucurbitaceae crops. The Python-based workflow developed in this study also offers a transparent, reproducible alternative to conventional CUB analysis approaches. Full article

Remote sensing research increasingly relies on heterogeneous satellite, UAV, hyperspectral, multispectral, SAR, and environmental monitoring data to support land, urban, hydrological, and environmental applications. However, these data are often affected by sensor differences, spatial and temporal heterogeneity, missing observations, irregular sampling, noise, and non-stationary environmental processes. This systematic review was conducted within the context of the Romanian Hub for Artificial Intelligence (HRIA) project, which supports the development of strategic artificial intelligence technologies. The review synthesizes current research on adaptive neural networks for remote sensing and Earth observation, with particular attention to Liquid Neural Networks and related continuous-time neural models. A systematic search was conducted across IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, Wiley Online Library, Google Scholar, and reference lists. After duplicate removal, screening, and full-text assessment, 61 studies published between 2018 and 2026 were included in the qualitative synthesis. The findings show that adaptive neural networks have gained increasing attention after 2022 and are mainly applied to image-centered remote sensing tasks, including classification, mapping, object detection, segmentation, enhancement, and change detection. Most studies adapt established deep learning architectures through multi-scale processing, adaptive feature fusion, attention mechanisms, graph relationships, or task-specific refinement. Continuous-time models are used less frequently but are relevant for irregular observations and dynamic environmental processes. Liquid Neural Networks remain emerging, and current evidence suggests only preliminary, task-specific relevance for irregular, noisy, multimodal, and dynamic remote sensing applications. Full article

Real-time automatic detection of fire and smoke is a critical component of modern safety systems in surveillance, industrial, and environmental-protection applications, where conventional sensor-based systems show fundamental limitations. In this paper, a comparative analysis of four deep-learning architectures—DETR, Faster R-CNN (ResNet-50-FPN), Faster R-CNN (MobileNetV2), and RetinaNet—was conducted on a heterogeneous corpus of 67,765 annotated images originating from four different datasets. All models shared the same data-preparation, augmentation, and evaluation pipeline, while each was trained with the default configuration of its reference implementation; the comparison therefore reflects each architecture as it is typically deployed rather than a comparison under a single unified training budget. Faster R-CNN with the ResNet-50-FPN backbone achieved the highest accuracy (mAP@0.50 = 78.7% on the Indoor set; 73.8% on the SmokeAndFire set) and the highest mean mAP@0.50 across all datasets (48.7%), obtained with an inference speed of 76.9 FPS and a latency of 13.5 ms on NVIDIA RTX 4090 hardware, which makes it a promising candidate for real-time fire-detection systems on comparable hardware. The main contribution of this work is a unified evaluation of four representative object-detection architectures across four heterogeneous fire-and-smoke datasets. The study further quantifies the performance asymmetry between fire and smoke detection through a normalized morphological asymmetry index, reflecting the consistently lower detection accuracy achieved for smoke owing to its diffuse and semi-transparent appearance, and provides practical guidelines for selecting an architecture according to real-time deployment requirements. Because each configuration was trained once with a fixed random seed and all speed measurements were obtained on a single desktop GPU, the reported differences are interpreted descriptively; their statistical validation, cross-dataset evaluation, and measurement on embedded hardware are identified as future work. Full article

Background/Objectives: Accurate intraoperative tumour localisation remains challenging in minimally invasive colorectal surgery because tactile palpation is lost and conventional markers can migrate or provide imprecise localisation. Building on a preceding tri-frequency study that identified 13.56 MHz as the preferred RFID band for the intended application, this work develops a shaft-compatible NFC antenna–reader platform and evaluates its electromagnetic behaviour from bench-top reference media to five-layer tissue-equivalent phantoms. Methods: A Ø3 × 25 mm Fair-Rite Material 67 ferrite-rod antenna was designed from material and geometric parameters using finite-rod demagnetisation, inductance, resonance, and field calculations, followed by FEM cross-validation and experimental characterisation. The primary dataset comprised 480 detection distance measurements (2 media × 4 tag angles × 30 repetitions × 2 encapsulation variants). Phantom testing added 1440 measurements at 22 °C and 600 measurements at 37 °C across three fabrication batches, with the 37 °C non-coaxial subset limited to one batch. Results: The fabricated antenna measured 16.9 µH versus a 17.4 µH analytical estimate (−2.9%), with loaded Q = 23. The coaxial detection range was 16.45 ± 0.29 mm in air and 16.26 ± 0.21 mm in saline; angle was the dominant determinant of range (partial η² = 0.989). In the multi-layer phantom, detection was 100% at 0 and 10 mm perirectal fat thickness under coaxial alignment at 22 °C, whereas performance declined markedly with angular misalignment and no detections occurred at fat thicknesses ≥ 20 mm. Across detectable phantom configurations, FEM showed r² = 0.994, RMSE = 0.81 mm, and mean bias +0.70 mm. Bare and resin-overcoated tags showed no statistically detectable range difference. Multi-tag discrimination reached 100% for up to three tags separated by ≥20 mm under coaxial alignment, but deteriorated with angular misalignment. Conclusions: The study demonstrates a physics-informed route from antenna miniaturisation to measured system performance, and defines the present operating envelope under controlled bench and tissue-equivalent phantom conditions. The electromagnetic measurements apply to the antenna–electronics subassembly; integrated-shaft, multi-prototype, multi-operator, ex vivo, and in vivo validation remain necessary before clinical performance can be determined. Full article

Background: A substantial proportion of patients experience persistent voice-related symptoms after thyroidectomy, even without clinically detectable laryngeal nerve injury. Identifying factors associated with delayed voice recovery is therefore important, as postoperative voice alterations may influence vocal performance and occupational voice use. This study examined the associations of patient-related (age, sex, body mass index [BMI]) and surgical variables (thyroid volume, type, and duration of surgery) with postoperative voice recovery stage within a six-month follow-up period. Methods: This prospective cohort study included 292 consecutive adults undergoing thyroidectomy at a tertiary referral center with normal preoperative voice and laryngeal findings. Patients with prior neck surgery, pre-existing voice disorders, reflux disease, or intraoperative laryngeal nerve injury were excluded. Objective voice assessment was performed multidimensionally at four predefined postoperative time points. The recovery stage was defined ordinally according to the first follow-up demonstrating normalization of all objective voice parameters. Multinomial logistic regression was used to examine multivariable associations and interactions between BMI and surgical variables. Results: Three regression models examined interactions between BMI and surgical variables, with recovery stage 4 (no normalization by six months) as the reference category. All three models were statistically significant (χ²(15) ≥ 86.32, p < 0.001). Duration of surgery was independently associated with recovery stage across models, with longer surgery associated with lower odds of recovery at stages 2 and 3 relative to stage 4 (p ≤ 0.041). Thyroid volume was also associated with recovery, with higher volume associated with greater odds of stage 1 versus stage 4 recovery (p = 0.008). Significant interactions were observed between BMI and duration of surgery (LR χ²(3) = 12.10, p = 0.007) and between BMI and type of surgery (LR χ²(3) = 15.17, p = 0.002), both driven by the comparison of stage 3 with stage 4. No significant interaction was found between BMI and thyroid volume (p = 0.294). Conclusions: Both patient-related (BMI) and surgical factors were associated with postoperative voice recovery stage. The association between BMI and recovery differed according to surgical duration and type, but not thyroid volume. These findings may support risk stratification and more tailored postoperative monitoring of objective postoperative voice recovery. Future multicenter studies using appropriate ordinal regression models and interval-censored survival methods are warranted to further clarify individualized recovery patterns following thyroidectomy. Full article

Continuous objective assessment of pig motor condition, including recovery after anesthesia, requires the joint analysis of posture, locomotion, transitions between states, and short-term adverse events. This study aimed to develop and algorithmically evaluate a multi-camera pipeline for the quantitative analysis of simulated recovery states in a fully synthetic virtual environment. Procedural generation was used to produce 150,000 images annotated with fifteen anatomical keypoints while varying the pose, size, and appearance of the model, camera viewpoints, illumination, environment, and image post-processing parameters. The YOLO11m-pose model was used for two-dimensional pose estimation, after which observations from three synchronized cameras were combined using weighted triangulation. The reconstructed three-dimensional trajectories were processed using a quality-control system, a finite-state machine, and temporal rules for detecting falls, prolonged immobility, and convulsion-like movements. After repartitioning the dataset by three-dimensional pose index, the retrained YOLO11m-pose model achieved a keypoint ${\mathrm{mAP}}_{50--95}$ of 0.8968, a precision of 0.9985, and a recall of 0.9986 on the independent pose-level test set. During the processing of a 15-minute three-camera sequence, 98.993% of 405,000 reconstructions satisfied the geometric acceptance criteria, and the median reprojection error was 2.420 pixels. On a first manually annotated 5-minute synthetic sequence, the state machine achieved a strict frame-level accuracy of 91.20%, a macro-F1 score of 90.10%, and Cohen’s $\kappa$ of 0.860; accuracy outside $\pm 1$ s neighborhoods of state transitions was 97.43%. On a second 5-minute synthetic evaluation sequence with exact Unity-world geometric ground truth, direct three-dimensional evaluation yielded an MPJPE of 31.86 mm at 99.993% landmark coverage. A limited temporal assessment on this second sequence detected both of the two lying-derived prolonged-immobility reference intervals; fall and convulsion-like-movement events were not independently annotated. All training, validation, and end-to-end evaluation data were generated in a virtual environment; therefore, these results establish algorithmic feasibility within the synthetic domain but do not establish performance on real animals. Full article

Background/Objectives: Sweet orange (Citrus sinensis (L.) Osbeck) is an economically important fruit crop that contributes substantially to food security and smallholder income in Mozambique. Despite this, the genetic diversity of the locally grown germplasm has not been characterized at the molecular level, limiting its improvement and conservation programs. This study assessed the genetic diversity and population structure of germplasm from 94 sweet orange trees sampled across four districts of Inhambane Province using DArTSeq single-nucleotide polymorphism (SNP) markers. Methods: After filtering 8111 SNPs for call rate (≥0.80) and minor allele frequency (≥0.01), 1263 markers were retained, of which 1144 were anchored to the nine chromosomes of the reference genome. Results: Sparse non-negative matrix factorization identified K = 1, indicating a single undifferentiated gene pool, supported by a smooth PCA scree with one weak axis. DAPC assigned individuals to their district only 38.3% of the time (random expectation = 25%; maximum a-score = 0.10), and the first two PCoA axes explained 10.33% of variation with complete district overlap, indicating no detectable geographic structure. Diversity was low, with observed heterozygosity (Ho = 0.247) exceeding expected heterozygosity (He = 0.138) and a negative inbreeding coefficient (Fis = −0.222). The pattern indicated a heterozygote excess consistent with the fixation of the heterozygous interspecific-hybrid genome under clonal propagation. A hierarchical analysis of molecular variance showed that differentiation among districts was negligible (0.04%), whereas 4.16% of variation was partitioned among orchards (farms) within districts, indicating that the little of the existing structure resides at the orchard level, confounded with propagation method and cultivar, rather than among districts. Most variation was partitioned within individuals (76.0%), and pairwise FST values (0.0005–0.0035) were uniformly low. Conclusions: These results indicate that the sweet orange orchards stem from a single, highly heterozygous gene pool redistributed through the exchange of seed and vegetative planting material. This underscores the need to introduce diverse external germplasm to broaden the genetic base for sustainable improvement in Mozambique. Full article

Background/Objectives: This study aimed to compare the diagnostic performance of multimodal (image-capable) large language models (LLMs) and oral and maxillofacial radiologists in detecting nine predefined incidental findings on panoramic radiographs, using a cone-beam computed tomography (CBCT)-derived reference standard. Methods: This retrospective diagnostic performance study included 500 purposively assembled, finding-enriched panoramic radiographs paired with CBCT images. CBCT images were evaluated by three radiologists to establish the reference standard. Two experts and three LLMs, the latter accessed through their consumer web interfaces, independently assessed the presence or absence of the nine findings; 4500 finding-level decisions were analyzed for each reader. Sensitivity, specificity, accuracy, and error rates were calculated. Within-patient clustering was accounted for using generalized estimating equations and a cluster bootstrap procedure with 5000 resamples. Finding-specific comparisons used Cochran’s Q test with Benjamini–Hochberg correction. Results: Agreement between the two experts was very good (κ = 0.86). Expert sensitivity, specificity, and accuracy ranged from 89.5 to 91.6%, 92.0–93.0%, and 91.8–92.9%, respectively, compared with 75.1–83.1%, 88.0–90.0%, and 86.8–89.0% for the LLMs. The overall reader effect was significant for all three performance metrics (all p < 0.001). In the exploratory high-risk group, expert sensitivity ranged from 90.9 to 93.2% versus 62.9–78.0% for the LLMs. After correction for multiple comparisons, the difference between readers remained significant only for carotid artery calcification (q < 0.001) and extensive maxillary sinus pathology (q = 0.005). Conclusions: Under the consumer-interface conditions and access period tested, the LLMs performed below the experts, particularly for high-risk findings, and should not be used independently to evaluate panoramic radiographs. Because the dataset was finding-enriched and single-center, the absolute estimates cannot be transferred directly to routine clinical populations, and any future role for these models, more plausibly as an expert-supervised adjunct or screening aid than as a replacement for expert interpretation, remains to be tested prospectively. Full article

Objectives: We aimed to determine quantitative photon-counting CT (PCCT)-derived iodine map (IM) attenuation associated with visual detectability of parenchymal hyperattenuation on conventional brain window (BW) images after mechanical thrombectomy (MT) and to assess corresponding relative BW attenuation changes. Methods: In this retrospective single-center study, 12 patients with anterior circulation large vessel occlusion underwent MT followed by post-interventional PCCT. BW, IM, and virtual non-contrast reconstructions were generated. Follow-up non-contrast CT served as reference for final infarction extent. Of 54 ASPECTS regions with final infarction, 40 showed IM hyperattenuation and were included in the detectability analysis. Two blinded readers assessed visual detectability. IM attenuation and relative BW attenuation increase (%ΔHU) were evaluated using ROC analysis with clustered bootstrap resampling. Thresholds were determined using the Youden index. Results: Across all 54 regions with final infarction, median IM attenuation was significantly higher in infarcted than contralateral regions (6.39 vs. 2.18 HU, p < 0.001), while absolute BW attenuation did not differ significantly. Among the 40 regions with IM hyperattenuation, 15 were visually detectable on BW images. ROC analysis showed excellent performance for IM (AUC = 0.997) and %ΔHU (AUC = 0.984). The optimal IM threshold in the original cohort was 8.33 HU (sensitivity 100%, specificity 96.0%), and the optimal %ΔHU threshold was 3.06% (sensitivity 100%, specificity 92.0%). Bootstrap-derived median thresholds were 11.69 HU (95% interval 8.33–12.67) for IM and 3.06% (3.06–20.16%) for %ΔHU. Inter-reader agreement was excellent (κ = 0.81–0.86). Conclusions: PCCT-derived iodine quantification enables objective assessment of visual detectability of post-interventional parenchymal hyperattenuation. In this cohort, an internally derived IM cutoff of 8.33 HU was associated with BW visibility. These preliminary findings require validation in larger, independent multicenter cohorts. Full article

Background/Objectives: Pharmacogenetic testing of the dihydropyrimidine dehydrogenase gene (DPYD) is increasingly incorporated into clinical practice to identify patients at increased risk of fluoropyrimidine-related toxicity. However, most routine assays target a limited number of well-established variants, whereas the DPYD gene demonstrates substantial population variability and may harbor rare potentially functional alleles that are not detected by conventional targeted testing. Data describing coding and splice-region DPYD variants detectable by whole-exome sequencing in Russian oncology populations remain limited. This study aimed to characterize the frequency and distribution of common, clinically relevant, and rare DPYD variants in a Russian cohort of patients receiving fluoropyrimidine-containing chemotherapy and to compare the observed allele frequencies with European and East Asian reference populations. Methods: Descriptive pharmacogenetic analysis was performed in 339 patients with malignant tumors treated with fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT), folinic acid, fluorouracil, and oxaliplatin (FOLFOX), or folinic acid, fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX) regimens. Whole-exome sequencing was performed using Illumina technology with exome enrichment by KAPA HyperExome and a sequencing depth of at least 100×. Sequence reads were aligned to the Genome Reference Consortium Human Build 38 (GRCh38) reference genome, germline variants were called using Genome Analysis Toolkit (GATK), HaplotypeCaller, and functional annotation was performed with Ensembl Variant Effect Predictor. DPYD variants were classified according to their population frequency, predicted functional effect, ClinVar annotations, and current pharmacogenetic recommendations. Allele and genotype frequencies were calculated and descriptively compared with Genome Aggregation Database (gnomAD) v4.1.1 Non-Finnish European and East Asian populations. Results: Seventeen DPYD variants were identified. The most frequent alternative alleles were rs1801265 (24.93%), rs1801159 (17.70%), rs2297595 (11.06%), rs1801160 (7.08%), and rs17376848 (5.16%). Their distribution was generally closer to that observed in the Non-Finnish European population than in East Asian populations. The established reduced-function variant rs67376798 (c.2846A>T, p.Asp949Val) was detected in one heterozygous patient, corresponding to a carrier frequency of 0.29% and an allele frequency of 0.15%. The HapB3 proxy variant rs56038477 (c.1236G>A) was identified in 13 heterozygous patients, with a carrier frequency of 3.83% and an allele frequency of 1.92%; confirmation of the functional intronic variant rs75017182 would be required for definitive HapB3 assignment. Overall, rs67376798 or rs56038477 was detected in 14 patients (4.13%). In addition, rare variants with a cohort allele frequency below 1% were identified in 11 patients (3.24%). Among these, p.Thr65Ala, p.Thr65Met, p.Asn151Asp, and p.Val691Leu represented potentially relevant findings requiring further functional validation. Conclusions: Whole-exome analysis revealed a heterogeneous spectrum of DPYD variants in the studied Russian oncology cohort, including both established pharmacogenetic markers and rare variants that would not be captured by limited targeted panels. The overall allele-frequency pattern was predominantly similar to that of European reference populations, although several rare variants demonstrated distinct distributions. These findings support the value of population-specific characterization of DPYD and suggest that expanded sequencing approaches may complement conventional pharmacogenetic testing by identifying rare potentially functional alleles. Full article

Background/Objectives: To develop and evaluate an automated CT-based framework for the quantitative assessment of fibrotic interstitial lung disease (ILD), including idiopathic pulmonary fibrosis (IPF), using a standardised six-level anatomical protocol and deep-learning lung segmentation. Methods: The segmentation dataset comprised 3315 manually annotated development slices from 92 patients and a non-overlapping internal holdout of 845 slices from 5 patients. A separate 100-study localisation/scoring set yielded a 97-patient agreement cohort (84 IPF, 13 other ILD; 1164 per-level, per-lung observations) after three DICOM-conversion exclusions. YOLO11n-seg masks underwent vessel- and structure-removal fibrosis detection. The radial spatial score was compared with a non-blind expert-adjudicated reference; the per-level Fibrosis Index was an auxiliary read-out. Results: On the five-patient internal segmentation holdout, mean intersection over union (mIoU) was 0.926 ± 0.017; the in-sample development value was approximately 0.95. Model-only latency was 73.8 ± 9.7 ms/slice at batch size 1, and peak throughput was 1.48 ms/slice at batch size 512. In the separate 97-patient agreement cohort, the expert-adjudicated score was identical to the automated score for 967 of 1164 observations (83.1%) and differed for 197 (16.9%). In the modified-score subset, Pearson r was 0.918, mean absolute error was 3.07, and ICC(2,1) was 0.889 (patient-clustered 95% CI 0.828–0.923). The pooled ICC(2,1) was 0.988 (0.982–0.992), but this value was inflated because the 967 unchanged pairs were identical by construction. Sequential end-to-end processing, measured in seven study patients, took a mean of 22.5 s per patient (median 24.0 s, range 17.3–24.9 s); localisation accounted for 88.1% of this time. Conclusions: The framework combined lung segmentation, anatomically standardised sampling, and automated fibrosis scoring. The radial score showed preliminary analytical concordance under non-blind expert adjudication. The fibrosis detector remains a proof-of-concept implementation based on 8-bit windowed images and has not been compared with independently drawn pixel-level fibrosis masks. The radial partition is an exploratory scoring convention and was not compared with alternative partitions or validated against clinical outcomes. Larger external studies using native Hounsfield-unit data and independent blinded readers are required. Full article

Background/Objectives: Conventional cardiovascular risk scores may provide only modest discrimination in asymptomatic individuals undergoing screening. Ultrasound markers, including resting left anterior descending coronary artery peak diastolic flow velocity (V-LAD), carotid plaque, and global longitudinal strain (GLS), may detect subclinical abnormalities not captured by standard clinical assessment. Methods: In this prospective single-centre screening study, 1100 consecutive asymptomatic adults underwent clinical evaluation, laboratory testing, transthoracic echocardiography with coronary Doppler and GLS analysis, carotid ultrasound, and treadmill exercise electrocardiography. The primary analysis used Cox regression for time to the first composite event of adjudicated new-onset angina, acute coronary syndrome, heart failure hospitalization, atrial fibrillation, or death. Hierarchical models sequentially added continuous V-LAD, carotid plaque, and GLS to an internal clinical reference model. Results: Among 1094 evaluable participants, 79 primary endpoints occurred over a median follow-up of 25 months. Harrell C-index was 0.653 for the clinical model and increased to 0.824 after addition of V-LAD. The combined V-LAD-plus-plaque model had a C-index of 0.816, V-LAD was independently associated with outcome (HR 1.32 per + 10 cm/s (95% CI 1.23–1.43), p < 0.001) and carotid plaque remained strongly associated (HR 4.19 (95% CI 2.37–7.41), p < 0.001). Adding plaque beyond V-LAD significantly improved model fit (likelihood ratio p < 0.001) but did not further improve discrimination. GLS added no incremental prognostic information. Conclusions: V-LAD provided the major incremental gain in discrimination beyond conventional clinical variables, while carotid plaque contributed independent prognostic information and improved overall model fit without a consistent further gain in discrimination beyond V-LAD alone. GLS provided limited incremental value. Full article

The operational behavior of micromobility users is a key indicator of the safety performance and design quality of cycling infrastructure; yet, existing video-based methods either require intensive manual processing or locate users coarsely through the centroid of the bounding box. This study presents and validates a semi-automated computer-vision tool that extracts the lateral position and instantaneous speed of micromobility users from bird’s-eye-view video recordings acquired with a single camera. The tool, implemented in Python 3.10.11, integrates bike lane segmentation, background-subtraction-based detection, multi-object tracking, and a heatmap-based contour extraction that places the measurement point at the wheel–pavement contact, providing a physically meaningful reference at predefined control sections. Validation was conducted in controlled tangent and curved sections, against physical distance references and previously verified e-scooter speed readings. In the tangent section, lateral position estimates showed a negligible bias, with a mean error below 1 cm and 99% of observations within ±5.0 cm, while over 70% of speed estimates were within ±2.0 km/h tolerance. In the curved section, the tool slightly underestimated lateral position and overestimated speed, with errors remaining within the practical tolerances. These results support the use of the tool for operational and safety studies of micromobility infrastructure under controlled conditions, reducing processing time without requiring trained detection models. Full article

Barrett’s esophagus (BE), the intestinal metaplasia arising from chronic gastroesophageal reflux disease, is the principal identifiable precursor of esophageal adenocarcinoma (EAC), whose incidence rose from 0.4 to 2.8/100,000 person-years between 1975 and 2017 and whose prognosis, once symptomatic, remains poor. Because outcomes depend on intercepting the metaplasia–dysplasia–carcinoma sequence, diagnostic accuracy is decisive. White-light endoscopy with Seattle-protocol biopsy remains the reference standard, yet it is constrained by the following three interrelated weaknesses: sampling error, as random forceps biopsies interrogate only about 3.5% of the Barrett’s mucosa; poor reproducibility of dysplasia grading, with interobserver agreement of only κ 0.24–0.27 for the pivotal distinction of low-grade dysplasia; and a substantial burden of missed disease, with roughly one-quarter of EACs diagnosed within a year of an index endoscopy reported as nondysplastic. This review synthesizes the technologies converging to address these gaps. Advanced imaging, encompassing high-definition endoscopy, narrow-band imaging, acetic acid chromoendoscopy, and the optical-biopsy platforms confocal laser and volumetric laser endomicroscopy, raises dysplasia yield by approximately 34% over standard white-light examination. Image-enhanced endoscopy improves targeted detection while remaining complementary to structured biopsy sampling. Molecular, genetic, and epigenetic biomarkers, notably DNA-content abnormalities, p53 immunohistochemistry, and multi-gene methylation panels, add an objective read on progression risk. Their pairing with non-endoscopic sampling, including Cytosponge-TFF3, capsule endoscopy, exhaled volatile organic compounds, and circulating microRNA liquid biopsy, is reshaping screening at population scale, while artificial intelligence standardizes interpretation and narrows the expert–nonexpert gap. Together these advances point toward a risk-stratified, multimodal paradigm, though prospective validation and cost-effectiveness evidence remain prerequisites for guideline adoption. Full article