Search Results (6,036)

Early diagnosis of Diabetic Retinopathy (DR) is critical for preventing irreversible vision loss, but precise lesion annotation by ophthalmologists is the dominant cost in building any clinical-grade DR detection model. The structural problem in real hospital settings is not labeling cost per se, but expert availability: ophthalmologists’ time is bounded by clinical duties, so the active-learning (AL) cycle can iterate only a handful of times in practice. We frame this constraint explicitly and ask which AL designs work best under a tight expert budget. We propose Virtuous Cycle, a Human-in-the-Loop (HITL) pipeline that integrates (i) a YOLOv8x-based object detector for microaneurysms, hemorrhages, and exudates, (ii) four AL sampling strategies (Average Confidence, Random, Hybrid-Diversity, Monte Carlo Dropout), and (iii) an in-hospital annotation platform (Diavision Studio) in which clinicians refine AI pre-labels rather than draw from scratch. We evaluate Virtuous Cycle on a real-world fundus dataset from the National Medical Center (NMC) across eight AL rounds, expanding the labeled pool from 81 images (R0) to 481 images (R8) within the actual expert-time budget of two ophthalmologists. Across three independent random seeds, random sampling dominates at cold start (mean mAP@50 $0.14\to 0.25$ over R0–R1), whereas Hybrid-Diversity converges to the highest mAP@50, Precision, and Recall by R7 (431 images; mAP@50 $0.40$, Precision $0.55$, Recall $0.41$), with MC Dropout close behind; by R8, the labeled pool is exhausted and all strategies converge to the same final model. A clinician crossover analysis of 36 paired clinical images, controlling for per-clinician speed bias and per-image difficulty bias, shows no statistically significant difference in overall per-image labeling time between AI-assisted and manual annotation ($p=0.52$), but a statistically significant increase in confirmed lesion detections under AI assistance ($p=0.0058$), driven predominantly (84–$100\%$ of the net increase) by microaneurysms, the lesion type most prone to being missed unaided. The results indicate that, under expert-budget constraints, AL strategy choice should be staged: random sampling for cold start, uncertainty-and-diversity sampling once the model has matured, and that AI assistance trades a modest, lesion-burden-dependent time cost for a measurable gain in the sensitivity of microaneurysm detection. Full article

With the advancement of smart city technologies and the proliferation of Mobility as a Service (MaaS), realizing seamless navigation that continuously connects heterogeneous mobility modes and indoor–outdoor spaces has emerged as a critical challenge. However, existing navigation services operate in a fragmented, siloed manner, segmented by transport mode and spatial environment, and thus possess fundamental limitations in supporting continuous mobility. This study establishes an analytical framework comprising the four core components of navigation systems (positioning, navigation data, route planning, and guidance) and critically reviews 108 prior studies identified through purposive sampling from Web of Science, Scopus, and Google Scholar to evaluate the technical requirements and the level of seamless integration achieved for each component. The analysis reveals that while each component has reached a high level of maturity within its individual domain, four critical technical gaps persist across all components: positioning handover discontinuities at indoor–outdoor transition zones, structural and semantic inconsistencies between heterogeneous spatial datasets, static route planning that fails to account for transition-space uncertainties, and guidance systems whose context resets upon changes in transport mode. These gaps originate not from insufficient performance of individual technologies but from a systematic lack of research at the interface points between components. Overcoming these challenges necessitates a comprehensive redesign of the integrated system architecture, encompassing dynamically adaptive multi-sensor fusion positioning, hierarchical heterogeneous data integration models, probabilistic cost modeling for transition spaces, and adaptive guidance systems based on automatic context handover. Full article

Periprosthetic joint infection (PJI) remains a major clinical challenge after total joint arthroplasty because of its association with prolonged antimicrobial therapy, repeated surgery, implant failure, functional disability, and substantial socioeconomic burden. Current strategies, including systemic antibiotics, debridement with implant retention, staged revision, and antibiotic-loaded cement spacers, remain indispensable but are limited by mature biofilm tolerance, protected microbial reservoirs, insufficient local drug penetration, persistent inflammation, and compromised periprosthetic bone repair. Increasing evidence indicates that PJI is not merely bacterial colonization of an implant surface, but a dynamic prosthetic interface disorder involving biofilm persistence, immune dysregulation, inflammatory osteolysis, and failed osseointegration. This review summarizes recent advances in anti-infective prosthetic interface design, emphasizing the transition from passive antibacterial coatings toward multifunctional immuno-antibacterial osseointegrative systems. The pathogenic basis of PJI is first discussed, including conditioning film formation, bacterial adhesion, biofilm maturation, protected reservoirs, immune evasion, and osteolysis. Current clinical management limitations are then evaluated, followed by emerging biomaterial strategies, including anti-adhesive and contact-killing surfaces, active antimicrobial coatings, mature biofilm disruption, biological antibiofilm therapies, smart infection-responsive delivery systems, and osteoimmunomodulatory interfaces. Particular attention is given to balancing early antibacterial activity with cytocompatibility, immune resolution, angiogenesis, mechanical durability, and long-term osseointegration. Finally, key translational barriers are highlighted, including load-bearing and tribological constraints, insufficiently standardized mature biofilm and animal models, limited clinical evidence for advanced smart materials, manufacturing reproducibility, sterilization compatibility, regulatory complexity, and application-specific clinical readiness. Future anti-PJI interfaces should evolve beyond unidirectional bacterial killing toward stage-specific systems integrating biofilm control, immune restoration, vascularized bone regeneration, and durable mechanical performance. Full article

Ovarian maturity in live female mud crabs (Scylla paramamosain) strongly affects harvest decisions and market value. Current ovarian maturity assessment relies mainly on expert-dependent methods that are subjective and destructive. Therefore, this study aimed to develop an interpretable, non-destructive image-based framework to classify crab ovarian maturity as immature or mature. A total of 240 crab image sets acquired using ventral external-view, dorsal external-view, and dorsal transillumination imaging were retained for analysis. Six primary morphometric features were semi-manually extracted from these views. External-view images quantified carapace width (CW), abdomen width (AW), abdomen area (AA), and sternum area (SA). Dorsal transillumination images yielded carapace area (CA) and ovary area (OA), an internal cue visualized through the intact carapace. To mitigate body-size variation, three ratio-based features—abdomen–carapace width ratio (ACWR), abdomen–sternum area ratio (ASAR), and ovary–carapace area ratio (OCAR)—were calculated. Between-class comparisons and correlation analyses were performed to guide candidate feature-set construction. Because OA and OCAR were strongly correlated, two reduced feature sets (Reduced 1 and Reduced 2) were designed to compare absolute ovary area with normalized ovary occupancy. Five feature sets—Raw, Ratio, Combined, Reduced 1, and Reduced 2—were evaluated using logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers. The Combined feature set, integrating all primary and ratio-based features, achieved the strongest mean cross-validated performance when paired with LR. On the held-out test set (n = 40), the final Combined-LR model achieved 0.950 accuracy and 0.997 ROC–AUC. On an independent practical implementation set (n = 40), the model correctly classified 39 specimens, achieving 0.975 accuracy. These findings may support non-destructive ovarian maturity screening and commercial grading in mud crab aquaculture. Full article

Dysregulated activation of the NLRP3 inflammasome is a key driver in the pathogenesis of numerous inflammatory disorders. This study aimed to evaluate the protective effect of Platycodon grandiflorus polysaccharide (PGPS_t) against NLRP3-inflammasome-mediated inflammation and elucidate its underlying mechanisms. An in vitro inflammatory model was established in porcine alveolar macrophages (3D4/21) using LPS/ATP co-stimulation. The effects of PGPS_t were assessed by measuring inflammasome activation, intracellular reactive oxygen species (ROS) generation, and pro-inflammatory cytokine secretion. Molecular docking, alongside inhibitors (NAC, MCC950) and siRNA targeting NEK7, was employed to probe the involved mechanisms. PGPS_t significantly suppressed NLRP3 inflammasome assembly and activation, reduced caspase-1 cleavage, and decreased the maturation and release of IL-1β and IL-18. It exerted its inhibitory effects through dual mechanisms: scavenging intracellular ROS and directly binding to NEK7 and NLRP3 to disrupt their interaction, as supported by molecular docking. The anti-inflammatory effect was diminished upon NEK7 knockdown. In conclusion, PGPS_t is an effective natural inhibitor of the NLRP3 inflammasome, functioning through ROS clearance and direct interference with the NLRP3–NEK7 interaction. These findings propose PGPS_t as a promising therapeutic candidate and further validate NEK7 as a potential target for treating NLRP3-driven inflammatory diseases. Full article

Background: Biological maturation is a major determinant of physical performance in youth football, although the previous evidence suggests that training context may influence maturation–performance associations. This study investigated the association between biological maturation and physical performance in youth football players from professional and non-professional settings. Methods: A total of 302 male football players (Under-10 to Under-14) from a professional academy (n = 122) and non-professional clubs (n = 180) participated. Biological maturation was estimated using maturity offset and age at peak height velocity (aPHV). Physical performance was assessed through standing broad jump, T-test agility, and sit-and-reach tests. General Linear Models and stratified correlation analyses were used to examine the interaction between maturation, age category, and training environment. Relative age distribution was also described. Results: Professional academy players demonstrated superior explosive power, agility, and flexibility across most age categories compared with non-professional players. Significant associations between biological maturation and physical performance were observed mainly in the non-professional environment, particularly for agility and explosive power, whereas few significant relationships emerged in the professional academy. Significant interactions between training environment, age category, and maturation status were found for all performance measures, with the strongest effect observed for agility. A relative age effect emerged only in the older professional categories. Conclusions: Associations between biological maturation and physical performance differed according to training environment in youth football players. Stronger maturation–performance relationships were generally observed in non-professional settings, whereas weaker associations emerged in the professional academy. However, due to the cross-sectional design and the likely interdependence between maturation, selection, and training exposure, causal interpretations cannot be inferred. Full article

Background: In the literature, the occurrence of menarche, the index of female sexual maturation, is related to chronological age (CA), but in conditions of abnormal growth it was proposed that determination of bone age (BA) was a better index of pubertal maturation than CA. Aim: To compare CA with BA at menarche in girls with Laron Syndrome (LS), a model of genetic IGF-I deficiency, which causes severe dwarfism and delayed puberty. Subjects: Data were retrieved retrospectively from our medical records. Nine untreated and four IGF-I-treated girls with LS were included in the study. Eleven GH-treated girls with congenital isolated GH deficiency (cIGHD) served as comparison. Results: The mean BA at menarche of untreated LS patients (13.4 y) was 0.7 y less than the CA (14.1 y), and the mean BA of IGF-I-treated girls (13.7 y) was 1.2 y less than the CA (14.9 y). Body height and weight did not influence the age at menarche. Conclusion: In girls with LS, the determination of BA seems a more useful index of female pubertal maturation than CA. Full article

Background: Antibody–drug conjugates (ADCs) have evolved from early heterogeneous constructs into a mature therapeutic platform with exponential clinical relevance. This review highlights recent advances in ADC design and development, with emphasis on antigen selection, antibody engineering, linker and payload innovation, site-specific conjugation, clinical translation, toxicity, resistance, and emerging data-driven approaches. Methods: The review draws on the literature published from 2019 to the recent clinical and regulatory developments relevant to approved and late-stage ADCs, emphasizing the advances in target biology, antibody formats, linker chemistry, payload classes, conjugation technologies, developability assessment, and computational or artificial intelligence-assisted design strategies. Results: ADC development has evolved with improved target selection, enhanced internalization and tumor selectivity, and the use of engineered, bispecific, biparatopic, and fragment-based antibody formats. Linker and payload innovation has expanded beyond traditional microtubule inhibitors to include topoisomerase I inhibitors, DNA-damaging agents, and emerging dual-payload or non-cytotoxic strategies. Site-specific conjugation and improved control of drug-to-antibody ratio have increased stability, pharmacokinetic performance, and manufacturability. Clinically, ADCs are being used across a broader range of malignancies and treatment settings, although toxicities and resistance mechanisms remain an important limitations. Computational methods and artificial intelligence are increasingly being explored for target discovery, molecular optimization, toxicity prediction, and model-informed clinical development. Conclusions: ADCs are transitioning toward a more integrated, design-driven platform in which antigen biology, antibody format, chemistry, and computational prediction are jointly optimized. Future progress will depend on improved standardization, biomarker-guided development, and interdisciplinary approaches to enhance its therapeutic index and expand its applications beyond oncology. Full article

Background: Diagnostic communication influences patient understanding, adherence, and shared decision-making in high-burden cardiometabolic disease and high-stakes oncologic care. However, scalable training models that allow standardized, repeatable practice and competency benchmarking remain limited. This study examined whether undergraduate medical students demonstrated higher diagnostic communication scores after completing a structured generative artificial intelligence (AI)-assisted simulation program across three clinically distinct diagnostic disclosure scenarios. Methods: We conducted a prospective, single-arm, pre–post educational study in undergraduate medical students completing AI-assisted diagnostic communication training across T2DM, obesity, and breast cancer scenarios. Students underwent baseline in-person assessments with standardized human simulated patients, completed 10 asynchronous AI-assisted encounters per scenario using standardized scenario-specific prompts and automated feedback, and then completed post-intervention in-person assessments. Scenario order was randomized. Performance was scored live by two physician raters using an adapted 24-item, eight-domain rubric. Cross-scenario analyses included three-scenario completers (n = 56; scenario-specific paired samples up to n = 77). Without a control group, analyses were interpreted as within-student pre–post associations rather than causal effects. Results: Students demonstrated higher post-test total rubric scores across all scenarios. Mean (SD) within-student changes were +24.26 (25.05) for T2DM, +26.17 (20.67) for obesity, and +36.31 (17.70) for breast cancer. Positive pre–post changes were observed across communication domains, with variation by clinical context. Exploratory analyses suggested limited cross-scenario gain-score associations and heterogeneous response patterns. Conclusions: Generative AI-assisted simulation was associated with higher post-intervention diagnostic communication scores across three diagnostic disclosure scenarios. The single-arm design precludes causal attribution and does not exclude testing effects, rubric familiarization, maturation, or concurrent clinical learning. Controlled studies are needed to determine its comparative educational value. Full article

Background: Hyaluronan (HA) is a critical extracellular matrix component that we have demonstrated to be important for embryonic endochondral bone formation and postnatal synovial joint formation, supporting normal articular cartilage (AC) architecture and chondrocyte function. Although the embryonic requirement for Hyaluronan Synthase 2 (Has2), the main HA-producing enzyme in skeletal tissues, has been extensively investigated, the cartilage-cell-specific roles of Has2 and HA in maintaining postnatal cartilage and joint integrity are not well-defined. Methods: In this study, we used a tamoxifen-inducible, cartilage-specific Has2 conditional knockout mouse model (AggrecanCreERT2Cre/+; Has2^fl/fl). A total of 20 male mice were collected, followed with tamoxifen administered at 3 weeks of age and tissues analyzed at early and late post-induction time points using histological and matrix-based assessments. Results: Administration of tamoxifen at 3 weeks of age resulted in near-complete absence of HA in AC and growth late (GP) at 4 weeks, one week after the induction, as confirmed by highly specific HA staining Hyaluronan binding protein (HABP) immunohistochemistry. These early changes establish that Has2-dependent HA synthesis is indispensable for maintaining matrix integrity, columnar organization, and postnatal GP maturation. We further extended these findings into later developmental stages, showing that by 11 weeks of age (8 weeks after induction), tibial joints exhibit AC surface irregularity, proteoglycan depletion, disrupted zonal architecture, and changes in the osteochondral unit consistent with early degenerative features. Conclusions: Taken together, these data suggest that HA deficiency triggered in early postnatal life is associated with increased cartilage vulnerability, supporting an important role for Has2 in cartilage maturation and long-term joint integrity. Full article

City information modeling (CIM) has developed from the extension of building information modeling (BIM) toward urban-scale information environments that combine geometry, semantics, spatial data, and analytical functions. CIM-related research is increasingly connected with BIM-GIS integration, semantic 3D city models, CityGML/CityJSON, smart-city platforms, and urban digital twins. However, previous reviews have mostly summarized themes, applications, and challenges, while less attention has been given to how CIM-related studies are operationalized through workflows, analytical functions, planning domains, validation evidence, temporal logic, and tooling. This study conducts a PRISMA- and BSMS-aligned systematic review of CIM-related research. Scopus and Web of Science searches retrieved 1541 records, which were deduplicated to 1101 records and screened through title, abstract, and keywords. The screening retained 318 records, of which 306 were accessible in full text. Eight additional eligible studies were added through supplementary snowballing, resulting in a final full-text extractable corpus of 314 studies. The results show that the largest workflow categories are data integration (W3, 95 studies, 30.3%), simulation (W4, 92 studies, 29.3%), and planning/governance support (W6, 84 studies, 26.8%). The largest planning domains are multi-scale/data infrastructure (P8, 91 studies, 29.0%), energy/environment (P5, 81 studies, 25.8%), and urban planning/governance (P7, 66 studies, 21.0%). Most studies were classified as applied (205 studies, 65.3%), followed by prototype (66 studies, 21.0%) and conceptual studies (43 studies, 13.7%). Validation evidence was dominated by empirical case studies (V3, 183 studies, 58.3%), while operational or practice-based validation remained limited (V4, 8 studies, 2.5%). Temporal analysis showed that dynamic and static studies appear in almost equal numbers, but full dynamic data use was reported in only 51 studies (16.2%). The review shows that CIM-related research is moving beyond conceptual framing, but operational maturity remains uneven. The main recurring gaps concern planning application, data quality, interoperability, dynamic data use, validation, and standardization. Full article

Geobacter sulfurreducens is a model bacterium widely used in microbial fuel cell (MFC) research due to its efficient extracellular electron transfer. However, the high cost of synthetic media limits the scalability of these systems, making agro-industrial byproducts like cheese whey a sustainable alternative. This study evaluated cheese whey as a growth medium for G. sulfurreducens and its influence on biofilm development on graphite bars electrodes. Bacterial growth kinetics and biofilm architecture were characterized using Atomic Force Microscopy (AFM) as the primary quantitative tool, supplemented by Scanning Electron Microscopy (SEM). Growth curves revealed a diauxic-like transition within the first 48 h, with high cell viability (94%). AFM analysis demonstrated a non-linear topographical evolution: an initial attachment phase was followed by a peak in structural heterogeneity at 14 days (S_q = 683.08 nm), eventually reaching a mature, confluent state at 21 days with a maximum thickness of ~8 μm. Energy-Dispersive Spectroscopy (EDS) confirmed an organic and mineral matrix consistent with bacterial biomass and whey components. These results demonstrate that cheese whey effectively supports the growth of G. sulfurreducens and the formation of structurally complex biofilms, highlighting its potential as a low-cost substrate for microbial cultivation and dairy waste valorization. Full article

Three-dimensional (3D) crop phenotyping is increasingly used to capture crop structure, but its value for crop protection is conditional rather than automatic. 3D approaches are operationally justified only when reconstructed geometry adds decision-relevant information beyond simpler 2D, spectral, scalar, or conventional baselines. This review examines 3D crop phenotyping through a reconstruction–trait–task–maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development. The literature is organized through four connected lenses: reconstruction routes that generate crop geometry, 3D traits that may alter protection reasoning, decision pathways that link traits to intervention variables, and maturity levels that distinguish static 3D models, validated phenotypic traits, process-coupled systems, protection outputs, and outcome-updated decision twins. The strongest decision-facing evidence currently comes from canopy-based dose adjustment, deposition prediction, drift reduction, and related spraying applications in which 3D traits are linked to intervention variables and field-facing comparators. Disease, stress, and architecture-aware modelling provide important but more heterogeneous evidence, while many point-cloud datasets, segmentation pipelines, neural reconstruction methods, and agricultural digital-twin frameworks remain upstream of practical crop-protection decisions because they do not yet connect 3D measurements to validated protection labels, comparator baselines, decision thresholds, intervention outputs, or outcome updating. A central conclusion is that high-fidelity 3D representation should not be conflated with decision-twin maturity. Protection-oriented digital twins require explicit coupling among synchronized crop geometry, functional or epidemiological models, decision rules, and recorded field outcomes. This review therefore identifies the evidence and reporting priorities needed to move 3D crop phenotyping toward validated, deployment-oriented, and feedback-aware crop-protection support. Full article

The energy transition in maritime shipping is increasing the importance of alternative fuels and port infrastructure capable of handling them in a safe, regular, and economically justified manner. In this context, LNG remains a transitional fuel with a relatively high level of technological and organizational maturity, particularly in regions characterized by intensive liner, ferry, and RO-RO traffic. This article proposes a universal model for organizing LNG distribution within the port–transport system, based on three interdependent dimensions: demand potential, infrastructure readiness, and operational feasibility. The model structure enables the classification of ports according to their functions within the regional bunkering network and the identification of nodes of the greatest systemic importance. The model was validated using data on vessel calls, the structure of container and RO-RO traffic, LNG infrastructure status, and monthly traffic variability. The analysis demonstrated that the most justified LNG distribution arrangement in the Baltic Sea is polycentric in nature and concentrated in ports, combining a high degree of transport regularity with confirmed LNG readiness. The results indicate that the rationale for LNG infrastructure development is selective in nature and depends on the actual position of a port within the transport network, rather than solely on cargo throughput volume. The proposed model also retains its applicability to other alternative fuels after adjustment of technological, regulatory, and operational parameters. By supporting the selective development of alternative-fuel infrastructure in ports with the highest systemic relevance, the model contributes to sustainable maritime transport planning and to the transition toward lower-emission port–transport systems. Full article

Mycobacterium abscessus subsp. massiliense (Mabs) is an emerging nontuberculous mycobacterium associated with difficult-to-treat infections due to intrinsic antimicrobial resistance, intracellular persistence, biofilm formation, and limited responsiveness to currently available therapeutic regimens. In this context, adjuvant strategies targeting iron-dependent metabolic pathways and metal homeostasis may enhance the efficacy of conventional antimicrobials. This study investigated deferoxamine (DFO), a clinically approved iron chelator, as a potential adjuvant against Mabs using integrated in vitro and in silico approaches. Cytocompatibility was assessed using an MTT assay in RAW 264.7 macrophages and a hemolysis assay in human erythrocytes. Antimicrobial activity was evaluated through minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays, while interactions with clarithromycin (CLA) and amikacin (AMK) were assessed using the checkerboard method. Effects on virulence-associated phenotypes were examined through biofilm formation assays and protein quantification in extracellular vesicle-enriched fractions. Intracellular activity and modulation of inflammatory mediator gene expression were investigated in Mabs-infected RAW 264.7 macrophages through colony-forming unit (CFU) recovery and reverse transcription quantitative polymerase chain reaction (qPCR). DFO exhibited low cytotoxicity and negligible hemolytic activity under the tested conditions. Direct antimicrobial testing revealed a predominantly bacteriostatic profile (MIC = 9.75 µg/mL; MBC > 10 mg/mL), whereas checkerboard analysis suggested a synergistic interaction with CLA (FICI = 0.047), which requires further confirmation by time-kill or CFU-based combination assays. Furthermore, DFO reduced biofilm biomass, decreased protein levels in vesicle-enriched fractions, lowered intracellular bacterial burden, and modulated cytokine gene expression in infected macrophages. Molecular docking, ADME/Tox, and PASS analyses generated exploratory hypotheses regarding potential molecular interactions and pharmacological properties. Overall, these findings support DFO as a promising experimental adjuvant candidate for further investigation against Mabs, particularly in combination with clarithromycin. However, confirmation of a putative iron-restriction-associated mechanism and its translational relevance will require validation in additional clinical isolates, iron-rescue experiments, mature biofilm models, and in vivo studies. Full article