PSMA-directed nanoprobe enabling precision imaging and MR-guided chemoradiotherapy in prostate cancer.

Accurate identification of the specific inner ear malformations can assist the otologist in anticipating surgical challenges and potentially optimizing postoperative hearing outcomes. In this study, we explored the feasibility of applying similar cochlear morphology principles, that is, ones based on easily quantifiable measurements of the basal turn, to differentiate normal lateral wall geometry from malformed variants. We retrospectively collected 60 patients who underwent cochlear implantation in our center between 2005 and 2023 in the presence of a preoperatively recognized inner ear malformation. Of the 120 analyzed cochleae, 111 were eligible for segmentation, which included 8 cochlear hypoplasia type II, 15 cochlear hypoplasia type III, 2 cochlear hypoplasia type V, 38 Incomplete partition type I, 44 Incomplete partition type II, and 4 incomplete partition type III. A control cohort of 141 normal cochleae was selected. Using manual segmentation of the cochlea on preoperative cone beam computed tomography scans, three-dimensional lateral wall spirals were obtained. The spirals were then used to compute simple anatomical measures of the basal turn, namely the cochlear diameter A and width B, the basal turn length computed based on A and B using the elliptic circular approximation approach, the ratio B/A and the B ratio, and cochlear height (H). Initially, two-sided Mann-Whitney-Wilcoxon tests were conducted to derive statistical differences in the aforementioned geometrical parameters between normal and malformed anatomies. Second, logistic regression analyses were performed to define whether the derived geometrical parameters may be used to predict if a specific cochlear morphology is normal or malformed, and to subsequently investigate if such a model may even be capable of distinguishing between different malformation types. Four geometrical basal‑turn parameters easily assessable in clinical imaging, namely the basal turn length along the lateral wall, cochlear height (H), B/A ratio, and B‑ratio, provide enough information to reliably distinguish normal cochleae from malformed variants. The binary logistic model achieved 94% overall accuracy with precision and recall ≥0.92 across classes, with cochlear height H emerging as the dominant predictor. In our models, H and Bb/B were the strongest discriminants for normal versus malformed anatomies and for incomplete partitions versus cochlear hypoplasia, respectively. Moving beyond binary discrimination, the three‑class model (normal versus IP versus CH) retained 90% accuracy. In summary, the models reliably recognize normal versus malformed, incomplete partition versus cochlear hypoplasia, and CHIII among individual subtypes due to reduced height. They are less reliable for IPI versus IPII differentiation, where basal‑turn measures alone appear insufficient, and IPIII, CHII, CHIV, where very small sample sizes depress recall. Simple, basal‑turn measurements of the lateral wall were demonstrated to provide a fast, interpretable, and accurate means to detect cochlear malformations on clinical imaging and to differentiate IP from CH. The approach reliably flags abnormal cases, offers actionable preoperative information for patient‑tailored implantation, and identifies domains where additional features or larger cohorts are needed (IPI versus IPII; rare subtypes). Such a model could lay the groundwork for future automated recognition of cochlear malformations during preoperative planning.

Ciprofloxacin is a widely studied fluoroquinolone antibiotic and serves here as a relevant test case for molecules exhibiting multiple protonation isomers (protomers) with abundances that vary depending on the ionization conditions using mass spectrometry (MS). Understanding the protonation behavior of such molecules during positive-ion electrospray ionization (ESI) or matrix-assisted laser desorption ionization (MALDI) is interesting from a perspective of fundamental ion chemistry, but also crucial in achieving efficient ionization, detection, and accurate identification in an analytical context. For example, different protomers may exhibit different fragmentation patterns in tandem MS applications. While previous research has utilized MS with ion mobility spectrometry (IMS) or infrared ion spectroscopy (IRIS) to analyze ciprofloxacin, this study integrates all three techniques by performing ion mobility-selected IRIS experiments to definitively assign the protomers of ciprofloxacin and of related piperazinyl building blocks. This combined approach exposes a subtle scenario in which the type of ion source and its mode of operation influence the protomeric structures that are produced and, moreover, where the downstream ion transfer and storage stages may induce proton-migration. These findings provide guidance for the development of general workflows for mass spectrometry-based assessment of analytes with multiple protomers.

Accurate identification and early warning of cognitive impairment after stroke.

Accurate identification and localization of axillary lymph node involvement in breast cancer before therapeutic interventions, such as neo-adjuvant chemotherapy or surgery, are for optimal disease management. This study evaluates the concordance between charcoal-based localization of axillary lymphadenopathy and sentinel lymph node biopsy. In this cohort study, 70 patients with breast cancer and up to three metastatic axillary lymph nodes were enrolled at Omid Hospital's radiology department. Nodes were marked with 0.2-0.3 mL charcoal injection under ultrasound guidance. After completion of neoadjuvant chemotherapy, preoperative lymphoscintigraphy was performed. The frequency of positive lymph nodes identified by charcoal staining was compared with the sentinel lymph node findings during surgery. Post-surgical pathology identified charcoal in 89 nodes across 64 patients, yielding a detection rate of 91.4% (95% CI: 85-98%). Among the 64 patients with charcoal- contained nodes, 56 were SLNs containing charcoal, and 8 were non-SLNs, resulting in a concordance rate of 87.5% (95% CI: 79.4%-96%). A chi-square test confirmed a significant association between charcoal-tattooed nodes and SLNs concordance (χ² = 12.3, p < 0.001). This study demonstrated that ultrasound-guided charcoal tattooing of metastatic axillary lymph nodes before Neoadjuvant chemotherapy is a safe, low-cost, and effective technique for localization of limited nodal involvement in breast cancer patients, with high concordance with sentinel lymph node biopsy.

Light-controlled RAA-Cas12a platform enables one-pot, ultrasensitive detection of vibrio parahaemolyticus in seafood.

Accurate identification and volume computation of molecular interaction regions over 3D triangular meshes.

Critical node identification in complex networks via gravity model based on steady-state restart Markov chain

Mandiblemath is an artificial intelligence-driven software for predictive mandibular reconstruction that infers three-dimensional mandibular morphology from two-dimensional craniofacial data. We developed and externally validated Mandiblemath using a modular Python implementation integrating scientific computing, 3D visualization, and supervised learning. Ninety-one computed tomography scans (46 female, 45 male) yielded 12 craniomaxillary angles per subject; partial least squares regression generated 12 patient-specific mandibular configurations (Y1-Y12). Sex-specific classifiers - random forest, XGBoost, linear SVM, RBF-SVM, and gradient boosting - were trained with 10-fold cross-validation. External validation used 18 independent scans (nine female, nine male). Geometric correspondence between predicted and real mandibles was quantified using standard surface-based metrics. The best internal classifiers were linear SVM for females (68% accuracy) and random forest for males (82.8%). External validation achieved 88.8% accuracy for group identification in both sexes and ranking accuracy (top-three suggestions) of 66.6% in females and 70.4% in males. Predicted models exhibited broad global congruence with reference mandibles, maintaining clinically acceptable discrepancies (RMSD 1.2-3.3mm) and strong overlap (F1@2.5 mm≥0.85; Surface Dice@2.5 mm≥0.80). Mandiblemath demonstrated technical feasibility and clinical potential as a decision-support tool, providing low-cost, reproducible predictions and printable meshes for reconstructive planning. The findings support future multicenter validation and integration into digital surgical workflows.

Sporotrichosis caused by Sporothrix brasiliensis is a highly virulent zoonotic mycosis rapidly expanding in Brazil, with increasing records in Pará since 2018. The reference diagnosis relies on culture and the time-consuming induction of fungal dimorphism, which delays taxonomic confirmation for weeks. The objective of the study was to develop and validate an in-house spectral library by MALDI-TOF MS for the rapid identification of S. brasiliensis in the Amazon region of Pará, prioritizing the feasibility of direct identification from the filamentous phase (primary growth). Thirty-seven Main Spectral Profiles (MSPs) from regional isolates were created, with 24 derived from the yeast phase and 13 from the filamentous phase. For validation, 46 molecularly characterized isolates were used in both filamentous and yeast-like morphologies, followed by the application of 39 routine clinical isolates obtained from primary culture. The library achieved 100% accuracy (score ≥ 2.0) in species identification for both fungal phases. Notably, all 39 routine clinical isolates were correctly identified directly from the filamentous growth, reducing the diagnostic time to an interval of 5 to 7 days. Although the yeast phase presented superior scores and greater proteomic stability, the mycelial signature proved robust for clinical use. The customized library overcomes the gaps in commercial databases and establishes a high-throughput workflow for the surveillance of S. brasiliensis. This technical optimization allows for more agile One Health surveillance, which is essential for epidemiological mapping and the control of zoonotic expansion in the region.

Brain image analysis presents significant challenges due to limitations in precision, computational efficiency, and interpretability. Although neural networks have proven effective for modeling complex patterns, they often function as black-box systems, making their predictions difficult to interpret and limiting their clinical utility. To address these challenges, we propose the adaptive spatial key-region identification (ASKRI) framework-a novel method to identify region of interest, which combines adaptive sampling based on Shannon entropy, probability-mean-driven selection, and spatial uncertainty quantified via kriging method. ASKRI integrates block-to-block kriging with statistical inference to interpolate CNN-derived classification performance, significantly reducing the computational burden of exhaustive model training without sacrificing predictive accuracy. Designed for seamless integration with convolutional neural networks (CNNs), ASKRI enhances both the accuracy and interpretability of ROI identification. Its effectiveness is demonstrated using the traumatic brain injury (TRACK-TBI) dataset, where ASKRI reliably identifies spatially consistent and biologically meaningful regions associated with aging. These results underscore the framework's potential to advance brain image analysis, while offering transparent and resource-efficient diagnostic support in clinical settings.

• Novel fusion method combines voltage harmonics using cluster quality weighting. • Perfect topology identification achieved with only 4–6 h of measurement data. • Method remains accurate with 10% measurement error and 60-min time resolution. • High-order harmonics outperform traditional RMS voltage for network identification. • Calinski-Harabasz index weights measurements by their clustering performance. Accurate identification of low-voltage (LV) network topology is becoming increasingly important, as reliable and detailed topological information is vital for effective network operation and precise modelling. Topology identification approaches based on smart-meter data typically rely on RMS voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instruments errors. This work introduces a novel methodology for distribution network topology identification through a multi-parametric analysis of smart-meter measurements. The core innovation lies in utilising the Calinski-Harabasz index (CH) as a weighting factor for multi-measurement distance matrices. The proposed framework integrates three distinct classes of measurements: V rms , harmonic components ( V 2 – V 20 ), and THD. The methodology addresses critical challenges in measurement-based topology identification approaches, including high measurement errors, short data collection time intervals, and large time resolution. The resilience of the methodology stems from a hierarchical approach that combines correlation analysis, cluster validation, and graph-theoretic network reconstruction. The results demonstrate significant improvement in the accuracy and robustness of network topology identification, compared to approaches based on single-measurement types.

Regarding the dependence of the traditional extended Kalman filter (EKF) algorithm on Gaussian measurement noise for identifying aerodynamic parameters in unmanned aerial vehicles (UAVs), an interacting noise model–based extended Kalman filter (INM-EKF) is proposed. Firstly, the longitudinal aerodynamic parameters of the unmanned aerial vehicle are selected as the identification object, and the identification system is modeled accordingly. The non-Gaussian measurement noise is approximated using a Gaussian mixture model. Subsequently, the expectation-maximization algorithm is employed to estimate the parameters of this model. Filtering calculations are then performed for multiple identification systems that incorporate Gaussian noise. The estimated values of the aerodynamic parameters are obtained by fusing the calculated results from each model. Finally, simulation experiments were conducted to compare the performance of the proposed method with three existing filtering techniques in environments characterized by Gaussian mixture noise and α-stable noise. The results indicate that the INM-EKF method outperforms the other techniques in terms of identification accuracy and convergence speed, significantly enhancing the accuracy of aerodynamic parameter identification for unmanned aerial vehicles.

Visual analysis of spatiotemporal density distributions is crucial for understanding spatiotemporal dynamics. However, existing methods suffer from visual occlusion and information loss when simultaneously displaying multiple density distributions. We present Strunkmap as an abstract approach to address these challenges. We introduce anisotropic kernel density estimation to enhance the accuracy of density generation. We extract the trunks of density distributions to identify the overall spatial patterns. Path scanning and trunk-outline matching strategies are employed to preserve local spatial structure. We design a stacked trunk plot that enables lossless density representation while conserving substantial screen space. Based on the visual design, Strunkmap integrates multiple heatmaps within a single map to effectively display temporal evolution of density distributions without visual occlusion. Ablation studies and comparative experiments validate the superiority of Strunkmap in accuracy and efficiency for hotspot identification and trend exploration. Theoretical analysis demonstrates Strunkmap's scalability, which we further verify through large-scale spatiotemporal data visualization. Color encoding schemes and scaling ratios are discussed to illustrate the flexibility. Our evaluations with user feedback demonstrate that Strunkmap is a viable solution with significant potential to real-world applications.

17 Background: Colorectal cancer management requires integration of complex genomic data, clinical guidelines, and patient-specific factors. Current clinical decision support systems often lack transparency and fail to ensure guideline adherence. We developed GenomAI, a multi-agent AI reasoning system that replicates prefrontal cortex decision-making to provide verifiable, guideline-based treatment recommendations. The platform uses a hybrid Cache-Augmented Generation and Retrieval-Augmented Generation architecture. Methods: GenomAI employs specialized multi-modal AI agents for diagnosis, biomarker analysis, treatment planning, and clinical trial matching. The system integrates NCCN guidelines with patient genetic profiles, pathology reports, clinical data, and imaging through HL7/FHIR-compliant APIs or document upload. We validated GenomAI using 100 retrospective colorectal cancer cases, evaluating: (1) accuracy of NCCN guideline identification and application, (2) appropriateness of clarifying questions when clinical information was incomplete, (3) concordance with multidisciplinary tumor board decisions, and (4) transparency through complete audit trails with exact NCCN page references. Performance was compared against direct-answer AI models. Results: GenomAI achieved 100% accuracy in identifying relevant NCCN guideline sections and 99% concordance with tumor board decisions. The system appropriately requested follow-up data in 14 cases with insufficient information. Compared to Claude Opus 4.5 and GPT-5.2 (85%), Gemini 3 Pro (82%), and MedGemma 1.5 (62%), GenomAI demonstrated superior performance. The platform integrated comprehensive clinical data: laboratory values, molecular profiling (KRAS, NRAS, BRAF, MSI, TMB), pathology, imaging, and colonoscopy while incorporating patient-specific factors including toxicity risks, comorbidities, and age. All oncologist-validated recommendations included NCCN page references and confidence scores. GenomAI reduced evidence synthesis time by 40% versus manual review and successfully identified eligible clinical trials. Conclusions: GenomAI demonstrates high accuracy in providing NCCN guideline-compliant colorectal cancer treatment recommendations. The multi-agent architecture ensures transparency, appropriate clarification-seeking, and refusal when guidelines are not applicable. This validation establishes GenomAI as a promising clinical decision support tool enhancing physician workflow while maintaining guideline fidelity and patient safety. Future work will expand to additional cancer types and prospective evaluation.

This narrative review aims to aggregate all reports of nosocomial transmission of rare yeast species, to provide an overview of global trends, causative species, clinical characteristics and preventive measures. A comprehensive literature search of multiple databases was conducted, to identify all reported nosocomial transmission events involving rare yeast species. The five most common yeasts, Candida albicans, Nakaseomyces glabratus, Candida parapsilosis, Candida tropicalis and Pichia kudriavzevii, in addition to Candida auris were excluded. A total of 76 reports were retrieved since 1984, caused by 28 different species. Wickerhamoyces anomalus was the most common agent, followed by Magnusiomyces, Trichosporon and species of the Candida haemulonii complex. Since the early 2000s, a clear increase was noted both in the number of outbreak reports and range of associated species, largely due to improved diagnostics. Most species were resistant to one or multiple antifungals and although environmental sampling was often performed, virtually all efforts yielded negative results. Once noted, strict hand hygiene practices and proper cleaning and disinfection of medical equipment has shown in curbing nosocomial spread. As hospital outbreak events are increasingly reported, accurate species identification and epidemiological surveillance should be in place for rapid detection. An enhanced focus on infection control measures was often sufficient to prevent new cases. Importantly, high-resolution genotyping is needed to confirm clonal transmission, which is often not conducted.

Lower Respiratory Tract Infections: Precision Diagnostics.

Cymbidium goeringii is one of the most widely cultivated and traded ornamental orchids in East Asia. Due to its high horticultural value and phenotypic variability, accurate cultivar identification is essential but challenging, as their flowers bloom only briefly in spring. We have developed a forensic tool for rapid and exact cultivar discrimination by applying 12 simple sequence repeat (SSR) profiles in C. goeringii. This study was performed to establish an expanded SSR dataset for cultivar identification and phylogenetics in C. goeringii. We examined a total of 6,051 samples from 269 cultivars, including 92 Korean cultivars with ≥ 10 samples each. Among these, representative combined genotypes (CG1) were determined, and their frequencies (CG1%) were used to assess genetic concordance among samples. Phylogenetic trees were constructed using both Euclidean and codominant genetic distances, and cultivar distributions were visualized using Principal Coordinate Analysis (PCoA) and t-SNE. Approximately 72.8% of the samples matched their dominant combined genotype (CG1), suggesting that nearly 30% of cultivated orchids may exhibit genotype discordance. Phylogenetics and PCoA showed a weak or no correlation between phenotypes, while they revealed relatively clear clustering between Korean and Japanese origins. These results highlight the value of integrating multiple analytical methods to enhance interpretability. The expanded SSR genotype dataset presented here offers a robust resource for cultivar identification, verifying genotype concordance, phylogenetic analysis, and ecological genetics research. This study will be an important milestone in the forensic application of plants with diverse cultivars exhibiting a wide range of horticultural and commercial values.

Lakeshores are both vital components of lake ecosystems and non-renewable resources that support human development. Understanding their utilization is essential for assessing human–lake interactions, yet accurate lakeshore type identification remains challenging because of the complex water–land interfaces and diverse natural and anthropogenic contexts of lakes. To address this, we developed RoLaSTIM (Robust Lake Shore Type Identification Method), an operational approach for classifying lakeshores as natural or artificial based on spatial analysis and land cover conditions. RoLaSTIM was applied to 12 typical global lakes, and their annual Shore Utilization Rates (SURs) were analysed from 2000 to 2022. RoLaSTIM achieved high classification accuracy (area-adjusted overall accuracy: 98.93 ± 0.02%, weighted F1 score: 98.94%), benefiting from a two-level buffer strategy and corresponding physical rules that minimize misclassification. Key factors influencing accuracy included the lake SUR, artificial lakeshore length, land cover complexity, and satellite image resolution. Moreover, we identified four distinct SUR evolution trends: increasing, stable, inverted (‘∩’-shaped), and fluctuating (‘M’-shaped). RoLaSTIM is suitable for robust and consistent lakeshore classification at large scales, supporting efficient SUR assessment and long-term monitoring. This method fills a critical gap in global lakeshore research and offers valuable insights for lakeshore management, conservation, planning, and sustainable development.

Hard ticks (Ixodida: Ixodidae) on humans in Nicaragua.