Development and validation of a VirB12-based indirect ELISA for differentiating Brucella abortus infection from A19-ΔVirB12 vaccination.

Proficiency in cytopathologic diagnosis depends heavily on extensive hands-on practice and immediate error correction. Traditional teaching models, however, are constrained by limited practice opportunities and delayed feedback, which fails to meet the core skill-development needs of residents. In total, 45 pathology residents were enrolled and assigned to two groups. The experimental group (n=20) adopted a tripartite teacher-artificial intelligence-resident collaborative teaching model, whereas the control group (n=25) received conventional instruction. Both groups underwent an identical 8-week teaching cycle. The questionnaire results from the experimental group indicated that 19 of 20 residents (9%) deemed the new model highly necessary, and 15 of 20 (75%) believed it significantly improved their diagnostic competence. Semistructured interviews further revealed that the model enhanced diagnostic ability, facilitated personalized learning, and alleviated learning anxiety. For objective metrics, the experimental group demonstrated a significantly higher postintervention concordance rate for gray-zone cell identification (78.65%) compared with both their preintervention baseline (64.38%) and the contemporaneous control group (66.84%; t=8.962; p<.001). In addition, the experimental group exhibited a markedly faster diagnostic speed (mean±standard deviation, 3.05±0.52 minutes per case) compared with their preintervention performance (5.92±0.85 minutes per case) and the control group (5.63±0.79 minutes per case; t=14.821; p<.001). No statistically significant changes were observed in the control group (p> .05). This study demonstrates that artificial intelligence technology integrated with real-time visual interaction effectively improves the cytopathologic diagnostic skills of residents and merits wider promotion in pathology education.

Cross-linking mass spectrometry (XL-MS) is a powerful tool for probing protein structures and protein-protein interactions. While chemical cross-linkers target specific residues with defined chemistry, photo-cross-linkers offer superior reactivity but have been hampered by incomplete mechanistic understanding and lack of robust analytical framework. Here, we demonstrate that diazirine-based photo-cross-links are inherently MS-cleavable, generating composite backbone and side-chain fragments, which have nevertheless confounded spectral interpretation. Yet by leveraging the side-chain fragmentation fingerprints (sFFP), we develop a machine learning model and subsequently, a rule-based filtering algorithm. When integrated with existing search platforms, our workflow significantly improves ion coverage and reduces false discovery rate for site identification. We further develop a homo-bifunctional diazirine cross-linker, allowing for cross-linking on-demand. This reagent captures transient tetrameric assemblies of human HSP90β and reveals structural transitions in association equilibrium under heat stress, details otherwise inaccessible with chemical cross-linking. Together, this work establishes a transformative framework in XL-MS, combining the temporal resolution of photo-activation with analytical confidence for residue-level structural insights.

Bloodstream infections represent a major medical emergency, in which rapid and accurate pathogen identification is essential for optimizing therapy. This study evaluates the analytical performance of the Autof MS2600 and Biotyper Sirius (Bruker) for the identification of microorganisms directly from positive blood culture. A prospective study was conducted over 7 weeks at the Brussels University Hospital laboratory, including all new episodes of bacteremia. Practical aspects and three preparation protocols were compared: a commercial kit and an in-house pretreatment protocol on the Autof MS2600, and an in-house pretreatment protocol on the Sirius system. A total of 157 bloodstream infection episodes were analyzed, including 64 Gram-negative bacteria, 90 Gram-positive bacteria, and 3 yeasts. Correct species-level identification rates were 62.4% using the Autobio kit, 73.2% using the Autof MS2600 with the in-house protocol, and 72.6% using the Sirius system. In-house protocols performed significantly better than the commercial kit (p < 0.05), with no significant difference between the two in-house protocols (p = 1.000). Performance was higher for Gram-negative bacteria (85.9% with in-house vs. 75.0% with the kit) than for Gram-positive bacteria (66.7% vs. 55.6%). The Sirius system showed the highest non-identification rate (25.5%), compared with the Autof MS2600 (11.5% with the in-house and 16.6% with the kit). The in-house pretreatment was faster than the kit-based protocol (19 vs. 40min). In-house pretreatment protocols significantly improve and accelerate direct microbial identification from positive blood cultures, with comparable performance between platforms. Differences in non-identification rates mainly reflect system-specific scoring thresholds and algorithmic approaches. In this context, the choice of platform may be based on practical considerations, although this should be interpreted in light of the study design and sample size.

Blood is indispensable in emergency medical care and serves as mortality. However, in disaster zones and other challenging environments, batch identification and rapid allocation of blood supplies remain particularly difficult. A portable blood batch identification device (PBBID) based on ultra-high-frequency (UHF) radio-frequency identification (RFID) technology was developed to improve the efficiency of blood verification management in complex environments. PBBID has a lightweight foldable cabin and an integrated composite protective layer and UHF RFID antenna and is equipped with a dedicated blood management terminal. Its function, identification rate and accuracy were tested, and blood quality was assessed via blood count, biochemical analysis and haemolysis rate assay. Folded PBBID occupies 24.92% of the expanded volume, unfolding takes 23.68 ± 6.00 s and can identifying 40 blood units in 5.37 ± 0.48 s. During the storage period, fixed-frequency scanning did not significantly affect the quality of red blood cells. However, intensive scanning exceeding 300 times led to a notable increase in the haemolysis rate. With its foldable design, rapid batch identification capability and operational simplicity, PBBID markedly enhances the efficiency and accuracy of blood management in emergency medical rescue. Under conventional conditions, it does not compromise blood quality and can shorten the time required for transfusion and emergency treatment of patients with trauma, potentially establishing it as a valuable tool in pre-hospital and emergency medical rescue operations.

Accurate sentinel lymph node (SLN) mapping is essential for staging melanoma and guiding adjuvant therapy. Conventional mapping uses technetium-99m (Tc-99m) radiocolloid ± blue dye. Near-infrared fluorescence with indocyanine green (ICG) offers real-time, radiation-free visualization but varies in transcutaneous performance. This review synthesizes contemporary evidence comparing ICG to Tc-99m and blue dye for SLN detection in melanoma, highlighting anatomic, technical, and safety considerations relevant to surgical workflow. Narrative review of prospective series, head-to-head studies, and recent systematic reviews/meta-analyses evaluating SLN identification rates, node-level detection, transcutaneous vs. intraoperative fluorescence, false-negative considerations, and adverse events, with special attention to head/neck melanoma and dual-tracer strategies. ICG achieves high patient-level SLN identification (≈79-100%), comparable to Tc-99m (≈86-100%) and consistently superior to blue dye alone. Dual-tracer approaches (ICG + Tc-99m or ICG + blue dye) frequently report near-universal detection and practical reductions in missed nodes. Transcutaneous fluorescence through intact skin is variable, limited by near-infrared penetration and BMI, whereas intraoperative fluorescence after a small incision is highly reliable and expedites dissection. Head/neck basins particularly benefit from real-time fluorescence to delineate complex channels and confirm first-echelon nodes. ICG demonstrates an excellent safety profile with rare hypersensitivity, avoids radiation exposure, and integrates smoothly with gamma-probe guidance. Adoption barriers include heterogeneous dosing/timing, equipment cost, and lack of standardized reporting. ICG fluorescence is a robust adjunct to standard SLN mapping, offering intraoperative "visual GPS" especially valuable in head/neck melanoma. Current evidence supports ICG as a complement for radiocolloid mapping.

Background:Understanding endoscopists’ perspectives and routine practice offers opportunities to improve bowel cleansing for colonoscopy.Objective:To elucidate Italian endoscopists’ perceptions of bowel preparation quality, focusing on defining high-quality cleansing (HQC) and its perceived benefits in clinical practice and for diagnostic outcomes.Design:Nationwide, cross-sectional, web-based survey.Methods:A nationwide, web-based cross-sectional survey was undertaken in Italy between August and September 2024 among gastroenterologists with special interest in endoscopy. Participants were recruited via telephone screening; of 498 gastroenterologists contacted, 150 respondents completed an online questionnaire; analyses were descriptive.Results:The survey results revealed that all respondents (100%) routinely evaluate and document cleansing in the endoscopy report and almost all (99%) used validated scales. The majority (72%) of endoscopists aimed for HQC, which they defined as a segment score of ⩾8–9 on the Boston Bowel Preparation Scale or ‘excellent’ on the Aronchick scale. Almost all (93%) considered HQC important in every colonoscopy regardless of indication. All respondents considered that HQC allows higher identification rates for adenomas and sessile serrated lesions, reduces procedure time, and improves overall clinical efficiency; 99% considered that HQC allows for more appropriate surveillance intervals. On a scale of 1–10 to rate confidence with the diagnostic reliability of the exam (1 = not at all confident, 10 = very confident), the respondents’ levels of confidence improved with high-quality bowel preparation; mean scores were 2.1 with inadequate preparation, 6.6 with good cleansing and 9.2 with high-quality bowel cleansing.Conclusion:The survey revealed that the vast majority of Italian endoscopists consider HQC essential across all clinical indications. The results support the transition from ‘good’ to ‘high-quality’ cleansing as the new standard in clinical colonoscopy practice.

Performance evaluation of VITEK® MS PRIME compared to VITEK® MS.

This study compares the rate of detection of clinically significant prostate cancer (csPCa) using multiparametric MRI (mpMRI)-informed microultrasound-guided (microUS) biopsy to historical published data using mpMRI-ultrasound (mpMRI-US) fusion biopsy at the same institution. A single-center retrospective study of patients who underwent mpMRI-informed microultrasound-guided (microUS) biopsy was performed. Positive predictive value (PPV) of targeted lesions by PI-RADS and PRI-MUS (Prostate Risk Identification Using Microultrasound) was calculated and compared to published data using an mpMRI-US fusion platform. 169 subjects and 244 total lesions were identified by mpMRI. 44/244 (18.0%), 167/244 (68.4%), and 33/244 (13.5%) were PI-RADS 5, 4, and 3, respectively. 206/244 (84.4%) lesions seen on mpMRI were identified on microUS. 26 additional lesions were identified by microUS only. PCa was identified in 120/169 (71.0%) patients, and csPCa was identified in 70/169 (41.4%) by targeted microUS-guided biopsy of MRI lesions. Targeted biopsy of lesions seen only on microUS added three cases of csPCa (73/169, 43.2%). PPV of targeted PI-RADS 5, 4, and 3 lesions for all PCa and csPCa was 0.80, 0.61, and 0.39, and 0.64, 0.31, and 0.12, respectively. Findings were not significantly different compared to historical data using mpMRI-US fusion biopsy (p > 0.05). PPV for csPCa was 0.18 for mpMRI lesions when no correlate was identified by microUS, compared to 0.37 for lesions when a correlate was seen (p < 0.05). This retrospective study demonstrates successful implementation of microUS-guided biopsy, with similar rates of identification of csPCa compared to historical data using mpMRI-US fusion. Question There is limited real-world comparison of the rate of detection of clinically significant prostate cancer using mpMRI-informed microUS-guided biopsy compared to historical mpMRI-ultrasound fusion biopsy. Findings Identification of clinically significant prostate cancer by microUS-guided biopsy was similar to mpMRI-US fusion biopsy, suggesting microUS-guided biopsy performs as well as mpMRI-US fusion biopsy. Clinical relevance Our study shows the complementary role of mpMRI and microUS for the detection of clinically significant prostate cancer, supporting the combined approach of these techniques.

Low dose vs. standard dose and administration time in near-infrared fluorescence cholangiography during laparoscopic cholecystectomy: Study protocol for a randomised clinical trial.

Introduction. Accurate and rapid identification of marine bacteria is essential for the precise diagnosis and treatment of infectious diseases caused by marine pathogens. The accuracy of microbial identification using matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOF MS) primarily depends on the diversity and number of strains included in the reference database.Hypothesis/Gap statement. We hypothesized that expanding the MALDI-TOF MS database with a broader collection of protein spectral profiles from marine bacteria would significantly enhance identification accuracy for these strains.Aim. This study aimed to establish a marine bacterial database to improve identification accuracy and to evaluate the applicability of MALDI-TOF MS-based cluster analysis for tracing the origin of clinical strains.Methodology. We collected 203 strains isolated from marine environments and clinical samples, acquired their MALDI-TOF MS spectra and constructed a mass spectral database specific to marine bacteria. To validate the accuracy of the expanded database, 80 external strains were subsequently tested. Furthermore, we assessed the strain classification efficacy of MALDI-TOF MS cluster analysis and phylogenetic trees constructed from gene sequences.Results. The species-level identification rate increased from 88.75 to 97.5%. The proportion of strains achieving a reliable identification score (>2.3) rose markedly from 43.75 to 91.25%. Cluster analysis based on MALDI-TOF MS demonstrated high accuracy in grouping bacteria at the species level. In addition, the maximum likelihood (ML) phylogenetic tree exhibited significantly higher bootstrap support values compared to the neighbour-joining tree.Conclusion. The expanded marine bacterial database markedly enhances the accuracy and reliability of MALDI-TOF MS for identifying marine pathogens. For species identification and traceability, we recommend a combined strategy that includes initial MALDI-TOF MS screening and verification with phylogenetic trees based on the ML method.

Laser capture microdissection (LCM) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables spatial proteomics at the few-cell level but is constrained by cumulative losses during specimen capture, surface adsorption during processing, and sample transfer prior to LC-MS/MS analysis. The capture-associated losses are particularly relevant for pressure catapulting systems such as the legacy Zeiss PALM MicroBeam, which, despite discontinuation, remains in active use and therefore requires compatible low-loss workflows. We present MR-SP2 (microreactor-based sample preparation for spatial proteomics), a one-pot workflow integrating reproducible Zeiss LCM-cut specimen capture, processing with minimized adsorptive losses, and pipetting-free transfer with Evotip disposable precolumns. The workflow was evaluated using a formalin-fixed paraffin-embedded (FFPE) murine kidney tissue analyzed by timsTOF flex LC-MS/MS analysis. Across 50,000 μm3 regions (22 cells), MR-SP2 modestly improved proteome depth (3381 ± 80 versus 3174 ± 59 proteins). Decreasing sample input further accentuated the advantage of MR-SP2 in maintaining higher identification rates, highlighting the successful reduction of the adsorptive losses. At 12,500 μm3 (5-6 cells), identifications increased to 1145 ± 188 versus 302 ± 126. At 3125 μm3 (1-2 cells), identifications reached 695 ± 112 versus 206 ± 51. MR-SP2 improves identification depth for few-cell FFPE samples by nearly 3-fold compared to conventional tube-based workflows.

Morbidity and mortality are significant risks associated with emergency laparotomies. A risk calculation tool facilitates the identification of high-risk patients and provides clinicians with information to help them make informed decisions. In search of an ideal scoring system that yields accurate results, we compared 30-day mortality predictions using the National Emergency Laparotomy Audit (NELA), the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (P-POSSUM), the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), and the Surgical Outcome Risk Tool (SORT) risk calculators. This retrospective study analysed data collected from adult patients who underwent emergency laparotomies between July 2018 to October 2019 at Maidstone and Tunbridge Wells NHS Trust. Each patient's median preoperative mortality risk was calculated using the four risk calculators: NELA, P-POSSUM, ACS-NSQIP and SORT. During the study period, 227 patients were eligible for inclusion, with a mean (sd) age of 65 (±16) years and a median American Society of Anesthesiologists score of 2. NELA and P-POSSUM identified 11 patients (sensitivity 73.3%) who died in the high-risk group, which was higher than the identification rates of ACS-NSQIP (53.3%) and SORT (40.0%). The average 30-day mortality risk for the 15 patients who died was 25.8% for NELA, 39.6% for P-POSSUM, 17.9% for ACS-NSQIP and 15.7% for SORT. NELA and ACS-NSQIP had the highest area under the curve at 0.869 and 0.877, respectively. Although NELA exhibited higher sensitivity (73.3%), ACS-NSQIP demonstrated greater specificity (88.7%). Overall, the NELA score demonstrated the highest performance in predicting mortality in emergency laparotomy.

The wind generation process is very sensitive to uncertainties based on environmental effects due to the rapid growing wind capacity. In the situation where no means of grid connection exist, the variation in power could compromise safety of power system operations. In this paper, a model of the wind-energy storage system with a doubly-fed generator model is presented. Seven groups of operating states are determined based on the frequency dynamic response performance of the wind-storage system. To enhance the recognition of wind-storage operating states, a novel model (CTM-Net) is proposed based on the combination of multilayer Transformer and Convolutional Neural Network (CNN). When a wind-power failure occurs, the probabilities of change in value of output between 0 and 4 MW and 76 and 80 MW are considered and energy storage devices are allocated considering the chargeable and dischargeable ratio of the system. It has been demonstrated that the wind-storage operating states have obvious variations at different sampling times. The faulty shutdown state (ZT2) identification rate is 99.57% which encourages the effectiveness of state recognition significantly. The operating state of the wind-storage system can be identified accurately using multilayer adaptive Transformer.

Addressing the issue of high cleaning loss rates encountered during actual combine harvester operations, this study designed a detection device specifically for monitoring cleaning losses. Initially, a three-dimensional model of wheat grains was established using Blender software. Subsequently, the impact processes of wheat grains and straw falling from different heights onto a sensitive plate were simulated using EDEM discrete element analysis software, from which contact force variation curves and motion trajectories were obtained. The results indicated a significant difference in the impact forces of the two material types on the sensitive plate, enabling material identification and loss rate calculation through signal acquisition. Based on these findings, a detection device comprising a mechanical structure and a control system was developed. An ESP32 microcontroller was employed to read data from piezoelectric ceramic vibration sensors. After processing the data with a Kalman filter, material classification thresholds were determined based on the principles of normal distribution. Preliminary experimental parameters were established through a three-factor, three-level experiment, and subsequently optimized using response surface methodology. The experimental results demonstrated that optimal threshold differentiation and the highest accuracy in loss rate calculation were achieved under the following conditions: a sensitive plate installation height of 550 mm, an inclination angle of 40°, and a conveyor belt speed of 8 m/min. Bench tests verified that the overall error of the device was less than 3%, with recognition rates exceeding 97% for both wheat grains and straw.

Tomatoes (Solanum lycopersicum) are not only a staple in cuisines worldwide but also a subject of scientific interest due to their health benefits and distinct ripening process. Recognizing the ripest and most flavorful tomatoes has led to innovative research combining technology and agriculture. In this context, image processing emerges as a promising tool to discern the quality of tomatoes, particularly through color analysis. This study explores the effectiveness of a region-based image processing system in identifying red, ripe tomatoes. Currently, this process is done by hand, which takes time and can lead to mistakes-developed a machine learning-based device that utilizes computer vision and image processing techniques to detect ripe tomatoes with high accuracy. By employing algorithms that analyze color, texture, and shape, our technology can identify the optimal harvest time, making the process faster, more efficient, and more cost-effective. Automating tomato harvesting is crucial to addressing the labor crisis and enhancing the effectiveness of the present harvesting process. The actualization of automated harvesting depends on the ability to precisely recognize fruits. Fruit that is harvested at its peak maturity has the maximum levels of taste, vitamins, and sale value, which optimizes financial gains. There is now an inadequate rate of identification and failure to identify because of the blockage of specific fruits by vegetation and unwanted fruits, as well as the color change brought on by light. In order to identify tomato fruits in difficult circumstances, this research suggests a tomato identification system using the enhanced YOLOv8 framework. According to the model's test evaluation, the YOLOv8-Tomato model's mAP0.5 was 86.9%, its recall rate was 98%, and its accuracy and precision were 94% and 90%, respectively.

Recent high-throughput applications to shotgun proteomics have shown great benefits of coupling ion mobility spectrometry (IMS) to mass spectrometry. IMS adds a separation dimension by differentiating biomolecules from their size and shape. We (and others) find that the distribution of the peptide collision cross section (CCS) is often bimodal, which limits the utility of current machine learning predictions for peptide identification. Molecular dynamics simulations indicate that the peptides in the drift tube can adopt multiple stable conformations and that the two modes correspond to predominantly extended (mostly helical) and more compact (globular and less ordered) conformations. Most peptides have a charge-dependent strong preference for one of the two conformations, while some can adapt to both, as evidenced by a simple geometric model of the CCS data. We suggest a novel two-valued CCS predictor that allows for multiple peptide conformations. Its integration into data-independent acquisition proteomics increases identification rates of peptides compared with single-value predictors.

This study aimed to elucidate the relationship between chemical profiles and in vitro biological activities of essential oils from Cladanthus eriolepis, Asteriscus graveolens, and Teucrium luteum subsp. flavovirens. The chemical composition of these essential oils has been previously reported in our recent publications, revealing distinct chemical profiles with pronounced interspecific variability and identification rates of 83.3%, 96.7%, and 98.1%, respectively. C. eriolepis exhibited a chemical profile rich in aliphatic esters with a mixed monoterpene–sesquiterpene composition dominated by α-pinene (13.0%), isobutyl angelate (10.7%), and 2-methylbutyl angelate (9.5%), whereas A. graveolens was characterized by a high abundance of 6-oxocyclonerolidol (72.5%). T. luteum subsp. flavovirens showed greater chemical complexity, including elemol (16.4%), α-pinene (12.0%), and eudesmol isomers (14.3%). All essential oils exhibited significant biological activities across various in vitro assays. C. eriolepis showed the strongest acaricidal effect (LC50 = 0.539 µL/mL) and notable inhibitory activities against acetylcholinesterase (AChE), tyrosinase, and α-glucosidase (78.4%, 74.6%, and 69.2%, respectively). T. luteum subsp. flavovirens displayed the highest antioxidant capacity (DPPH IC50 = 51.60 µg/mL; FRAP IC50 = 35.53 µg/mL). Overall, variations in chemical profiles strongly influence biological activities, highlighting their potential as multifunctional bioactive sources.

Among the mycotoxins of health and economic importance, aflatoxins hold a prominent place. This study aims to determine the chemical composition of the essential oils of Melaleuca leucadendra and Melaleuca quinquenervia, and to evaluate their effectiveness in the in vitro control of an Aspergillus flavus strain isolated from peanut seeds in Senegal. Essential oils were extracted from air-dried leaves collected in Fatick and the Mbao Classified Forest using hydrodistillation with a Clevenger-type apparatus. Chemical characterisation was performed using gas chromatography (GC-FID) and gas chromatography–mass spectrometry (GC-MS). Data were statistically analyzed using ANOVA and Student–Newman–Keuls tests at a 5% significance level. The chemical composition analysis of the essential oils revealed high identification rates of 96.8% for Melaleuca quinquenervia and 99.9% for Melaleuca leucadendra. The antifungal assays demonstrated a strong dose-dependent inhibitory effect of both essential oils against Aspergillus flavus ThC2. M. quinquenervia exhibited the highest activity, with inhibition rates ranging from 71.8% at 100 ppm to 92.3% at 1000 ppm. M. leucadendra showed moderate activity, with inhibition ranging from 44.2% to 63.0% across the same concentrations. Overall, the results indicate that both essential oils, particularly that of M. quinquenervia, exhibit strong antifungal potential against A. flavus. Essential oils derived from Melaleuca quinquenervia and Melaleuca leucadendra exhibit distinct chemical compositions that contribute to notable biological activities. These oils demonstrate strong inhibitory effects on mycelial growth and sporulation of Aspergillus flavus, along with a moderate capacity to reduce aflatoxin biosynthesis. Owing to their diverse bioactive compounds and multi-target mechanisms of action, certain essential oils display antimicrobial properties that can surpass those of conventional synthetic agents while reducing the likelihood of resistance development in microorganisms. Consequently, these natural products show significant potential as effective alternatives in integrated biological control strategies aimed at managing crop and stored-product pathogens, particularly aflatoxin-producing fungi.

Identification of unknown individuals is challenging, and radiological imaging databases provide rich anatomical information for automated recognition. This study evaluated whether a single routine magnetic resonance imaging (MRI) slice contains sufficient person-specific features to identify individuals in large databases. It analyzed 11,078 head MRI examinations from 5770 individuals (age 52 ± 18 years, 2714 men) acquired between 2002 and 2025. For identification, 112 individuals were randomly selected across eight 10-year age groups, and one slice from four anatomical regions was extracted. The remaining 10,966 MRI examinations with 247,804 slices formed the reference database. Distinctive anatomical features were automatically extracted using computer vision (CV), and the identification rate was evaluated by rank. Using a single MRI slice, the identification rate at rank 1 reached 96% (107/112) for the best-performing region, the maxillary sinus, among 5770 potential identities. Across all regions, the rank 1 identification rate ranged from 91% to 96%; combining them increased rank 1 and 10 identification rates to 98% (110/112) and 99% (111/112). Identification rate remained stable over several years, with only two cases showing reduced rank 1 performance, likely due to age-related morphological changes. A single MRI slice contains stable, individualized features sufficient for reliable identification in large databases, supporting automated CV-based personal identification across years.