Accurate dosage calculation is vital for patient safety, making effective instructional methods a priority in nursing education. This study investigated whether embedding images of medications or syringes in medication calculation exams affects students' performance and metacognitive judgments. In a randomized controlled trial, 120 students took a calculation exam either with images (n = 63) or without (n = 57). Performance and ease of learning judgments were analyzed using t-tests, analyses of covariance, and logistic regression. The image group achieved significantly higher scores (M = 7.22 vs. 6.04/8, P <.001) and reported greater perceived ease of learning P <.001). The benefit was pronounced for early-semester students. The effect of the image remained significant after controlling for native language. Calibration accuracy showed no significant difference. Images in medication dosage calculation exams were associated with improved performance and higher perceived ease of learning among nursing students. Incorporating images may better support the mastery of medication calculation.
Sydenham's chorea (SC) is the most common form of acquired chorea in childhood and a neurological manifestation of acute rheumatic fever (ARF). It is characterized by involuntary, rapid, and irregular movements. Although the diagnosis is clinical, neuroimaging can play a valuable complementary role. SC arises from an autoimmune response triggered by a streptococcal infection, targeting the basal ganglia. In some cases, it may be the sole manifestation of ARF. We present two pediatric cases with acute hemichorea. In one of them, chorea was the only clinical manifestation. Brain MRIs showed punctate ischemic lesions compatible with postinfectious vasculitis, allowing exclusion of other causes of vasculopathy and movement disorders. Both patients were treated with valproic acid and aspirin as an antiplatelet agent. Clinical and radiological improvement was observed in both cases, allowing treatment withdrawal after six months without symptom recurrence. La corea de Sydenham (CS) es la forma más frecuente de corea adquirida en la infancia y una manifestación neurológica de la fiebre reumática (FR) aguda. Se caracteriza por movimientos involuntarios, rápidos e irregulares. Aunque el diagnóstico es clínico, la neuroimagen puede cumplir un rol complementario valioso. La CS surge como resultado de una respuesta autoinmune desencadenada por una infección estreptocócica, dirigida contra los ganglios basales. En ciertos casos, puede ser la única manifestación de FR. Se presentan dos casos pediátricos con hemicorea aguda. En uno de ellos, la corea fue la única manifestación clínica. Las resonancias magnéticas (RM) mostraron lesiones isquémicas puntiformes compatibles con vasculitis postinfecciosa, permitiendo descartar otras causas de vasculopatía y de trastornos del movimiento. Ambos pacientes fueron tratados con ácido valproico y aspirina como tratamiento antiplaquetario. Se observó una buena evolución clínica e imagenológica, lo que permitió suspender el tratamiento a los seis meses sin recurrencia de los síntomas.
The diagnostic performance of artificial intelligence (AI) in real-world settings remains uncertain, particularly across different fundus camera systems. This study evaluates the diagnostic accuracy of three AI algorithms for diabetic retinopathy (DR) detection using two nonmydriatic fundus cameras, assessing image gradability and DR severity. A prospective diagnostic accuracy study was conducted at a primary health center, Khijrabad, Punjab, India (March-July 2021). The study evaluated three commercially available artificial intelligence algorithms for DR detection using two nonmydriatic fundus cameras. Participants underwent two-field, nonmydriatic fundus imaging with both cameras. Image quality and DR presence were independently assessed by masked human graders, including optometrists and a retina specialist. Diagnostic performance was measured using sensitivity, specificity, and positive and negative predictive values. A total of 272 images from 136 participants (mean age 67.7 years; 62% female) were analyzed. Human graders classified more than 97% of images as gradable, with DR detected in 47% of the images. AI-1 demonstrated the highest sensitivity (Forus: 97.5% (0.956-0.994); Intuvision: 81.7% (0.768-0.867)) but comparatively low specificity: 62.7% (58.6-66.8) and 53.8% (0.474-0.602). AI-2 displayed a balanced performance (sensitivity 80.0% and 77.0%; specificity 95.7% and 92.0%). AI-3 had a moderate sensitivity (73-80%) with the specificity ranging from 82% to 86%. AI performance varied across camera platforms, highlighting the need for context-specific validation to ensure safe integration into primary care and guide DR screening guidelines.
Here, we present a protocol to acquire high resolution, extended depth of field images of insect specimens by photographic focus stacking using a modular digital imaging system. The method provides a standardized workflow linking equipment assembly, calibration, image acquisition, and post processing. Using a full frame mirrorless camera (61 MP) coupled to microscope objectives and synchronized strobe illumination, the protocol achieves pixel scales from 0.76 m-0.19 m and produces artifact free composites through sub-micron focus increments (0.2 m). The procedure can capture and process approximately 20 final images per week under routine laboratory conditions. Compared with existing stacking solutions, this low-cost hybrid setup (< 30% of the cost of commercial systems) maximizes accessibility while maintaining diffraction limited image quality. Representative applications include the production of color calibrated identification plates for taxonomy, biodiversity digitization, and outreach. The protocol's standardized structure facilitates reproducibility across laboratories and field stations, supporting large scale insect imaging campaigns in both resource limited and institutional environments.
Sagittal split ramus osteotomy (SSRO), a widely used mandibular procedure in orthognathic surgery, may affect the masticatory muscles through subperiosteal dissection and soft-tissue manipulation. Although minimally invasive modifications aim to reduce surgical trauma, their long-term effect on masseter muscle microvascular perfusion remains unclear. This cross-sectional observational split-mouth study compared minimally invasive SSRO (MISSRO) and conventional SSRO using superb microvascular imaging (SMI). Twenty-one patients who underwent bilateral SSRO for mandibular advancement and completed at least 18 months of follow-up were included. In each patient, the conventional Hunsuck technique was performed on one side, while MISSRO was performed on the contralateral side. Microvascular perfusion was quantified using the vascular index (VI) calculated from SMI images with ImageJ software; masseter muscle thickness was then measured on B-mode ultrasonographic images. The minimally invasive side showed significantly higher microvascular perfusion than the conventional side (VI: 13.72 ± 7.43 vs. 8.24 ± 7.33; p = 0.001), whereas masseter muscle thickness did not differ significantly between sides (9.71 ± 1.87 mm vs. 10.27 ± 1.89 mm; p = 0.052). These findings indicate higher long-term masseter microvascular perfusion on the minimally invasive side; however, in the absence of preoperative or normative reference values and of functional correlates, the clinical significance of this difference remains to be determined.
Lung cancer, the predominant kind of cancer, needs considerable care, since inadequate treatment may lead to fatal outcomes. The integration of computer-aided diagnostic (CAD) systems is crucial for the early detection of lung nodules, greatly aiding in the decrease of death rates associated with lung cancer. Magnetic resonance imaging (MRI) is a proficient technique for diagnosing lung cancer. Diverse techniques have been investigated for the detection of lung nodules in computed tomography (CT) images. The diagnosis process is greatly influenced by the physician's expertise, which may lead to the unintentional neglect of specific patients and ensuing consequences. Deep learning has become a significant and well-established method in several fields of diagnostic medical imaging. This study introduces a deep learning approach for identifying lung nodules in magnetic resonance imaging. Automatic feature extraction and classification are accomplished via the use of a deep convolutional neural network (CNN) of 11-layers. A strategy for intensity normalization is established as a preprocessing measure, which, when integrated with data augmentation techniques, has significant effectiveness in identifying and categorizing benign and malignant lung nodules. The assessment of the suggested methodology included 243T2-weighted MR images acquired from the First Affiliated Hospital of Shenzhen University. The experimental findings provide a diagnostic accuracy of 98.5% and a Dice similarity coefficient (DSC) of 97.1% in differentiating benign from malignant lung nodules. The findings indicate that the newly designed model exceeds existing state-of-the-art approaches in terms of accuracy and operational efficiency.
Deep neural networks (DNNs) offer highly promising neurocomputational models of the visual system, yet vast gaps remain between DNNs and human observers. By some accounts, DNNs are approaching near ceiling levels in their ability to predict human neural responses to clear real-world images. However, even modest diversions toward more ambiguous viewing conditions can readily expose the brittle and inflexible nature of these networks. Human vision remains robust when faced with noise, blur, occlusion, and other challenges, whereas DNNs trained to classify large image datasets typically lack such robustness. Here, we discuss the ecological vision hypothesis, proposing that the robustness of human vision is acquired via learning from prevalent encounters with challenging viewing conditions, such that DNNs trained with similar challenges should become more robust and human-aligned. In particular, the prevalence of blur in everyday vision may enhance sensitivity to global shape and attenuate reliance on local textural cues. We conjecture that providing DNNs with ecologically relevant information to learn 3D scene and shape properties will further advance DNN-to-human alignment.
Recent advancements in large language models (LLMs), such as GPT-3.5, GPT-4, and Chinese models like ERNIE and SparkDesk (SPARK), have shown remarkable potential in story generation. However, a direct comparison between ChatGPTs, ERNIE, and SPARK (pretrained with dominant Chinese Corpus, referred to herein as C-dominant LLMs) remains underexplored in this domain, particularly regarding narrative writing continuation ability. Identifying such difference would be beneficial to teachers to adopt LLMs in writing practice. This study addresses this issue by comparing ChatGPTs and C-dominant LLMs in story continuation tasks, with a focus on cohesion, creativity, and formal linguistic competence. We harnessed advanced analytical tools, including Coh-Metrix and CTAP for cohesion and LIWC-22 and CLIWC for creativity, to assess the performance of GPT-3.5, GPT-4, ERNIE, and SPARK across English and Chinese continuations. Results indicated that C-dominant LLMs surpassed ChatGPTs in referential and deep cohesion for English story generation, while ChatGPTs excelled in latent semantic analysis. Conversely, ChatGPTs demonstrated superior performance in cohesion across all metrics for Chinese story generation. In terms of creativity, GPT models behaved better in image for English texts and voice for both English and Chinese, whereas C-dominant LLMs excelled in image for Chinese continuations. For formal linguistic competence, GPTs showed better performance in English tasks, while C-dominant LLMs in Chinese tasks. These findings provide insights for optimizing LLMs in multilingual contexts and offer guidance for their application in writing and translation based on diverse languages.
The authors report a rare case of a complex wide-necked saccular middle cerebral artery (MCA) bifurcation aneurysm with atherosclerotic plaque at the neck, which mimicked a fusiform aneurysm. A 68-year-old female presented with a 1-month history of dizziness. Angiography showed a fusiform aneurysm arising from the superior division of the left MCA. Contrast-enhanced black-blood MRI demonstrated marked aneurysmal wall and lumen enhancement, absent on noncontrast images. On surgical opening, a wide-necked saccular aneurysm was instead found. A calcified/atheromatous plaque was present at the aneurysm neck, which explained the radiological findings of a fusiform-looking aneurysm. The surgical strategy consisted of complex clip reconstruction with distal revascularization via an extracranial-to-intracranial (EC-IC) bypass. In this case, preoperative angiography suggested a fusiform MCA aneurysm, but intraoperative examination revealed a wide-necked saccular aneurysm. The presence of atheromatous plaques at the aneurysm neck can distort its angiographic appearance and pose a challenge for clip reconstruction. Performing a preoperative vessel wall MR study may improve characterization of aneurysmal morphology and support tailored management of complex cases. https://thejns.org/doi/10.3171/CASE2684.
Deep vein thrombosis (DVT) is the formation of thrombi in the deep venous system, most often in the lower extremities. Although usually not life-threatening, DVT requires timely diagnosis to prevent complications such as pulmonary embolism and post-thrombotic syndrome. The growing demand for image interpretation has generated interest in applying artificial intelligence (AI) to automated DVT detection. This scoping review analyzes the performance of artificial intelligence in diagnosing DVT using computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). We conducted a search across seven databases from inception to May 2025 using terms related to deep vein thrombosis, artificial intelligence, and machine learning. Eligible studies were limited to those evaluating DVT diagnosis using CT, MRI, or ultrasound. Two independent reviewers selected eligible studies, and quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Eleven studies published between 2021 and 2025 met the inclusion criteria. Some of the AI algorithms included RetinaNet, Deep R-Belief Neural Networks, and Sooty Tern Optimization. US-based models were the most studied algorithms, with sensitivities and specificities ranging from 68 to 100% and 70-100%, respectively. The MRI-based model achieved sensitivities, specificities, and accuracies of 95% to 97%. One CT-based model demonstrated a sensitivity of 83%. Studies evaluated across multiple imaging datasets showed high sensitivities, specificities, and precision of 96% or higher. Future research should prioritize multicenter validation and integration of clinical factors. In addition, explainable frameworks capable of integrating multiple imaging datasets must be developed with attention to workflow efficiency and cost-effectiveness to support clinical translation. The results indicate that AI is best situated as a supplementary tool rather than a replacement for expert interpretation in DVT diagnosis.
Quantitative, spatially resolved analysis of gene expression is essential for assessing cell-type-specific molecular profiles. In the Drosophila visual system, extensive genetic tools open a framework for direct evaluation of both RNA and protein levels in defined neuronal populations. Here, we present a step-by-step protocol that combines expansion-assisted HCR-smFISH (hybridization chain reaction single-molecule fluorescence in situ hybridization) with immunohistochemistry to enable quantitative analysis of cell-type-specific molecular profiles in genetically defined visual system neuronal types. The workflow is optimized for cells labeled with nuclear-localized or membrane-bound markers, allowing measurement of transcript and protein levels in the same neurons. Following tissue expansion, samples are imaged using light-sheet microscopy for rapid volumetric acquisition, with an alternative mounting and imaging workflow demonstrated for standard inverted laser scanning and spinning disc confocal microscopes. We further provide an automated segmentation algorithm that distinguishes nuclear and cytoplasmic transcripts, enabling analyses of transcriptional state and subcellular RNA localization. Practical guidance is provided on experimental parameters and common pitfalls affecting signal quality, tissue integrity, and quantitative performance. Representative applications include validation of cell-type-specific RNA interference by quantifying corresponding changes in RNA and protein levels. By enabling integrated RNA- and protein-level measurements with cell-type specificity, this approach provides a scalable strategy for hypothesis-driven molecular analysis and, in targeted contexts, a practical alternative to single-cell transcriptomic assays. This protocol provides a practical approach for validating cell-type-specific molecular perturbations while preserving the anatomical context of the intact Drosophila brain.
Mucus plugs have been previously recognized as an important pathological feature in asthma and COPD, but their clinical role in these diseases has not been explored in-depth until recently. Mucus plug formation is driven by mucus hyperconcentration, changes in mucus viscoelastic properties, impaired clearance, and mucociliary collapse. Scoring systems, such as the bronchopulmonary segment mucus plug score, have been used to associate greater mucus plug burden with poor clinical outcomes. Additional scoring methods obtained through quantitative image processing are currently under development. Mucus plug burden has been associated with greater exacerbations and spirometric decline in both asthma and COPD, as well as greater mortality in COPD. Recently, mucus plug burden has been used as an endpoint in clinical trials to evaluate the effectiveness of biologic therapies in asthma; multiple biologic therapies demonstrated decreases in mucus plug burden and associated improvements in spirometry with treatment. Together, this data suggests mucus plugs may be a treatable trait in asthma and COPD. Mucus plug burden has current clinical, phenotypic, and predictive utility and shows promise as a future biomarker. Increased incorporation into clinical trials, expanded evidence of treatment effect, and standardization of methodology and imaging protocols will be needed. CT-detection of mucus plug burden is ready for greater incorporation into both research outcomes and clinical care.
To make adaptive decisions, it is often necessary to retrieve episodic memories, for example, about whether an item was previously associated with reward. Compared to young adults, older adults are impaired at making adaptive episodic memory-based choices. There are substantial individual differences across older adults, however. In this study, we examined whether hippocampal volume or extrahippocampal cortical thickness in the medial temporal lobe (MTL) is associated with better episodic memory-based decision making in cognitively unimpaired older adults. Older adults (n = 87; aged 61-88) completed a decision-making task and a T1-weighted anatomical MRI scan. In the task, they studied images of houses paired with arbitrary reward values ($5 or $0). Later, they made incentivized approach/avoid decisions about these items. Finally, their memory for the items and their values was assessed. MTL structural data were segmented with the ASHS-T1 pipeline to obtain volume measures for anterior and posterior hippocampus, and cortical thickness measures in entorhinal cortex, parahippocampal cortex, and perirhinal cortex Brodmann areas (BAs) 35 and 36. BA35 cortical thickness was associated with better performance in the decision-making task. This effect persisted after adjusting for memory performance, suggesting that BA35 thickness is specifically linked to the retrieval and use of episodic memories at the time of choice. Although this study is cross-sectional, it suggests that atrophy in BA35 may contribute to subtle changes in the ability to use memory to make reward-maximizing choices, even in individuals who do not yet show evident cognitive impairment.
Ectopic suprasellar pituitary neuroendocrine tumors (Pit-NETs) are exceptionally rare lesions that differ from intrasellar Pit-NETs in both imaging appearance and surgical management. This paper presents 2 cases of intra-stalk ectopic suprasellar Pit-NETs and 2 cases of extra-stalk ectopic suprasellar Pit-NETs. Cases 1 and 2 are ectopic suprasellar Pit-NETs of the intra-stalk type, meaning they originated from and grew within the pituitary stalk. Preoperative imaging revealed an indistinct pituitary stalk. The tumors completely occupied the stalk, compressing it into a thin, attenuated structure, which made it extremely difficult to preserve the integrity of the stalk. In contrast, cases 3 and 4 are ectopic suprasellar Pit-NETs of the extra-stalk type. Although these tumors also originated from the stalk, their growth predominantly occurred outside of it. Preoperative imaging clearly visualized the pituitary stalk in these cases. Despite the compression caused by the tumors, the integrity of the stalk was preserved. Preservation of stalk integrity is more difficult in intra-stalk than in extra-stalk ectopic suprasellar Pit-NETs. Since the intra-stalk type carries a higher risk of postoperative hypopituitarism, regular follow-up observation is recommended for asymptomatic patients. Given the risk of postoperative hypopituitarism, surgical intervention for the intra-stalk type should be approached with caution. https://thejns.org/doi/10.3171/CASE26198.
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Mondor disease is a rare, benign and self-limiting entity, characterized by superficial thrombophlebitis of veins of the anterior thoracoabdominal wall. Its etiology is unknown, although predisposing factors are associated. Clinical presentation is a unilateral painful cordlike subcutaneous induration. The diagnosis is clinical, although can be confirmed with imaging studies. Symptomatic treatment is suggested and its evolution is selflimited. We present the case of a patient with Mondor disease triggered after physical exertion. Diagnosis was confirmed by ultrasound. Thrombophilia or neoplasm were ruled out. Condition improvement was complete. La enfermedad de Mondor es una entidad rara, benigna y autolimitada, caracterizada por la tromboflebitis superficial de venas de la pared toracoabdominal anterior. No debe confundirse con el síndrome de Mondor, una entidad obstétrica grave caracterizada por hemólisis, coagulación intravascular diseminada y shock séptico, habitualmente asociada a aborto séptico. Aunque su etiología es desconocida, hay factores predisponentes asociados. Clínicamente se manifiesta como un cordón unilateral doloroso indurado subcutáneo. El diagnóstico es clínico, aunque se puede confirmar con estudios de imagen. El tratamiento es sintomático y su evolución es autolimitada. Se presenta el caso de una paciente con enfermedad de Mondor que se desencadena luego de realizar esfuerzo físico. La ecografía resultó útil para confirmar el diagnóstico. Se descartó trombofilia o enfermedad neoplásica. Evolucionó con resolución completa del cuadro.
Pneumonia is one of the primary causes of morbidity and mortality in the world, especially in children, the older population, and patients with critical conditions. The traditional imaging methods, including chest X-ray (CXR) and computed tomography (CT), are associated with diagnostic drawbacks in sensitivity, radiation exposure, cost, and availability. Lung ultrasound (LUS) has emerged as a promising bedside imaging modality for identifying pneumonia because it is portable, safe, and can be used for real-time diagnosis. The aim of this systematic review and meta-analysis was to assess the diagnostic validity of LUS for pneumonia across heterogeneous patient groups and conditions. Studies published within the period of January 2015 to December 2025 were searched using a systematic literature search in PubMed/MEDLINE, Embase, Scopus, Web of Science, and Cochrane Library. The progress of removing LUS from pneumonia diagnosis using a reference standard was incorporated in studies. Information on true positives, false positives, true negatives, and false negatives was obtained. The quality of the study was determined using the QUADAS-2 tool. The pooled sensitivity, specificity, likelihood ratios and diagnostic odds ratios (DOR) were estimated using a bivariate random-effects meta-analysis model. The systematic review included 53 studies and 13,847 patients; 44 of these (7969 patients) were included in the quantitative meta-analysis. The pooled sensitivity of LUS in the diagnosis of pneumonia was 93.9 (95% CI: 90.9-96.0), and the pooled specificity was 84.9 (77.8-90.0). The pooled likelihood ratio was positive (6.23), and the negative likelihood ratio was 0.07 (95% CI: 0.047-0.108). The total diagnostic odds ratio was 87.43 (95% CI: 45.90166.54), which represents excellent diagnostic performance. Lung ultrasound has high diagnostic accuracy for pneumonia in a wide range of patient populations and clinical conditions. LUS is a useful diagnostic tool that can be used as an adjunct to, or even as an alternative to, traditional imaging in selected clinical settings, as it is portable, does not involve radiation, and has strong rule-out capability. Pneumonia is a serious lung infection, and accurate diagnosis is important for safe and timely treatment. This study reviewed many research papers to evaluate how well lung ultrasound, a safe scan that uses sound waves, can detect pneumonia across different groups of people. This study found that lung ultrasound has high accuracy for diagnosing pneumonia and performs well in identifying and ruling out the condition. This matters because it can provide a safe, low-cost way to help diagnose patients, especially where other scans are harder to access.
Congenital generalized lipodystrophy (BerardinelliSeip syndrome) is a rare autosomal recessive disorder characterized by near-complete absence of adipose tissue and severe metabolic disturbances. Early diagnosis is crucial to prevent complications. We present a 7-year-old female with no relevant family history, showing facial dysmorphism, diffuse skin hyperpigmentation, and deep abdominal folds. Skull radiography revealed structural abnormalities of the cranial base and mandible compatible with bone dysplasia. These clinical and radiographic findings supported the diagnosis of congenital generalized lipodystrophy. Despite normal leptin levels and a whole exome sequencing without pathogenic variants, the phenotype justifies clinical suspicion, suggesting the need for complementary genetic techniques to rule out large rearrangements. The association of facial dysmorphism, skin abnormalities, and craniofacial radiographic findings is highly suggestive of this rare disorder. Radiological imaging provides complementary evidence that strengthens clinical suspicion and helps differentiate it from other dysplasias. La lipodistrofia congénita generalizada (síndrome de Berardinelli-Seip) es una enfermedad rara de herencia autosómica recesiva, caracterizada por ausencia casi total de tejido adiposo y alteraciones metabólicas graves. Su diagnóstico temprano es esencial para prevenir complicaciones. Se presenta el caso de una niña de 7 años, sin antecedentes familiares, con rasgos dismórficos faciales, hiperpigmentación cutánea y pliegues abdominales profundos. La radiografía de cráneo evidenció alteraciones en la base craneal y mandíbula compatibles con displasia ósea. El hallazgo clínico-radiológico orientó hacia lipodistrofia congénita generalizada. A pesar de presentar niveles de leptina normales y un exoma completo sin variantes patogénicas, el fenotipo justifica la sospecha clínica, sugiriendo la necesidad de técnicas genéticas complementarias para descartar grandes rearreglos. La combinación de dismorfismos faciales, alteraciones cutáneas y hallazgos radiográficos en hueso craneofacial es sugestiva de este trastorno. Las imágenes aportan evidencia complementaria que refuerza la sospecha clínica y permiten diferenciarla de otras displasias.
Accurate, non-destructive assessment of wood chemical composition is important for evaluating the quality and utilization of valuable Phoebe woods, but conventional chemical analyses are destructive and cannot describe spatial heterogeneity. In this study, short-wave infrared hyperspectral imaging (SWIR-HSI, 900-1700 nm) was used to predict and map cellulose, hemicellulose, and lignin in Phoebe zhennan and Burma Phoebe. Spectral preprocessing, wavelength selection, and regression modeling strategies were systematically evaluated, and a cascaded CNN-Transformer model was developed to integrate local absorption features with long-range spectral dependencies. Full-spectrum SNV data provided the best input for cellulose prediction, whereas SPA-selected wavelengths improved the prediction of hemicellulose and lignin. The optimal models were SNV-CNN-Transformer for cellulose (R2p = 0.6784, RPD = 1.7867), SPA-SNV-CNN-Transformer for hemicellulose (R2p = 0.7719, RPD = 2.1213), and SPA-SNV-CNN-Transformer for lignin (R2p = 0.7973, RPD = 2.2503). Pixel-level prediction maps further visualized the spatial distribution of the three components across wood cross-sections. These results indicate that SWIR-HSI combined with CNN-Transformer modeling provides a promising approach for non-destructive chemical assessment and spatial characterization of valuable Phoebe woods.
The brain's capacity for integration arises from both its structural wiring and energetically demanding electrochemical signaling. Yet current connectome analyses treat network nodes as functionally homogeneous, ignoring that neural communication is constrained by metabolic cost. Here, we introduce a metabolism-weighted connectome, a fully weighted brain graph in which both connections and the metabolic activity of each node describe the network's capacity for integration. Using three datasets of simultaneous fMRI and [18F]Fluorodeoxyglucose positron emission tomography acquisitions, we define metabolism-weighted centrality (MwC), a biologically grounded index of each region's signaling dominance that integrates functional connectivity with local energy metabolism. MwC provides a more accurate representation of cortical activity flow than classical edge-based metrics and reveals that metabolically active hubs align with higher-order cognitive networks. Transcriptomic and synaptic imaging data demonstrate that these hubs exhibit increased synaptic energy turnover, linking activity-driven centrality to the molecular architecture of signaling. Notably, the same high-MwC regions show greater susceptibility to neurodegenerative pathology, suggesting that lifelong metabolic demand influences both integrative function and disease vulnerability. By linking neuronal metabolism to network organization, our framework bridges cellular energetics and system-level computation, opening broad avenues for interpreting brain vulnerability and performance.