Search Results (366)

In this study, taking NdCl₃ and 2-amino-1,4-benzenedicarboxylic acid (H₂ATA) as raw materials, a novel lanthanide metal–organic framework, Nd(ATA), was synthesized by the coprecipitation method. After loading antibiotic levofloxacin (LEV), Nd(ATA) was combined with GelMA hydrogel to prepare a drug-loaded composite hydrogel, LEV@Nd(ATA)-Gel, which can emit near-infrared fluorescence under excitation at 808 nm and possesses improved mechanical properties compared to pure GelMA hydrogel. LEV@Nd(ATA)-Gel exhibited high bactericidal activity and low cytotoxicity, with cell viability increased by 35% compared to the control group. The release rate of the loaded LEV was found increasing with the pH decreasing from 7 to 3, and demonstrated a potential responsiveness to wound microenvironment. Furthermore, drug delivery studies revealed a significant correlation with the fluorescence intensity of the composite hydrogel and the drug release behavior, and the extent of drug release was quantitatively captured by an in vitro imaging technology. This study successfully integrated the drug release with fluorescent signal of carrier, providing a highly sensitive and visualizable strategy for the development of internal wound adhesive. Full article

Selective hearing in multi-talker environments remains a central challenge for assistive listening technologies: conventional devices improve audibility but cannot infer which talker the listener intends to follow. This Perspective examines the emerging transition from auditory attention decoding to neuro-steered selective hearing. We argue that a recent real-time intracranial proof-of-principle study marks an important inflection point in this transition, because it shows, under high-fidelity recordings from neurosurgical patients and in controlled two-talker conditions, that decoded neural attention can be coupled online to relative-gain control, thereby improving speech perception and reducing listening effort within that experimental setting. Building on this proof of principle, we argue that the field should move beyond offline decoding accuracy as its primary benchmark and instead treat neuro-steered hearing as a closed-loop translational problem linking neural inference, acoustic scene analysis, and control policy. From this perspective, future benchmarks should integrate device-level control metrics, including latency, effective switch time, false-switch rate and recovery after erroneous updates, with listener-centered outcomes such as speech intelligibility, listening effort, user preference and everyday benefit. Accordingly, we outline a staged roadmap for electroencephalography-based systems, from controlled two-speaker paradigms to scene-aware control and ultimately wearable real-world implementations. We also discuss the complementary roles of magnetoencephalography, functional near-infrared spectroscopy, and functional magnetic resonance imaging in defining the neural mechanisms and design constraints of future systems. Overall, this Perspective frames neuro-steered selective hearing as a systems-level research pathway for evaluating whether neural attention signals can be translated into practical assistive listening technologies. Full article

Background/Objectives: Intraoperative molecular imaging (IMI) enables real-time visualization of tumor biology during surgery using fluorescent probes and near-infrared imaging systems. As lung cancer screening increases detection of small and nonpalpable pulmonary nodules, conventional localization and margin assessment techniques remain limited, particularly during minimally invasive surgery. This review summarizes the technical foundations, imaging agents, clinical applications, and future directions of IMI in thoracic oncology. Methods: We performed a narrative review to synthesize current evidence regarding the technical foundations, molecular imaging agents, clinical applications, and future directions of intraoperative molecular imaging in thoracic oncology. Given the multidisciplinary scope of the field, a narrative approach was selected to integrate mechanistic, translational, and clinical evidence rather than to answer a single narrowly defined clinical question. Results: IMI generates dynamic intraoperative contrast based on preferential probe accumulation or activation within malignant tissue. Current approaches include non-specific fluorophores such as indocyanine green, activatable probes targeting tumor-associated proteases or acidic microenvironments, and receptor-targeted agents such as pafolacianine. Across prospective studies and multicenter trials, IMI improved localization of nonpalpable lesions, identified occult synchronous malignancies, and enhanced intraoperative margin assessment, frequently altering surgical management. Phase 2 and 3 studies of folate receptor-targeted imaging demonstrated clinically significant findings in a substantial proportion of patients, including lesions not detected by conventional imaging or palpation. However, performance remains dependent on tumor biology, target expression, lesion depth, and optical constraints. Conclusions: IMI represents an emerging transition from anatomy-guided toward biology-informed thoracic surgery by providing real-time molecular information during resection. Current evidence supports its role as a complementary intraoperative technology that augments conventional imaging and surgical techniques, particularly for small, peripheral, and nonpalpable lesions. Full article

Tumor localization during pulmonary surgery has become increasingly challenging with the earlier detection of smaller and smaller lung nodules. Concomitantly, minimally invasive surgical (MIS) techniques have been increasingly adopted within the field of thoracic surgical oncology. Surgeons face growing challenges not only with locating these small tumors, but also with immediate margin assessment, reduced tactile feedback, and nodal assessment. Intraoperative molecular imaging (IMI) has emerged as a promising adjunct to address these challenges by enabling real-time visualization of malignant tissue during pulmonary resection. In its current form, IMI integrates systemically administered, tumor-targeting near-infrared fluorophores with fluorescence-capable imaging platforms to enhance intraoperative decision-making. Early clinical experiences in thoracic surgery suggest particular utility in the localization of small or nonpalpable pulmonary nodules and for improved margin assessment during MIS. Despite encouraging preliminary data, widespread adoption of IMI remains limited by biologic variability in target expression, optical depth constraints, false-positive fluorescence in inflammatory tissue, and challenges in workflow integration. Applications for nodal evaluation, staging, and longer-term oncologic outcome improvement remain investigational. Addressing these multifaceted barriers will be essential for the translation of IMI from a promising, experimental adjunct to a more broadly implementable surgical technology. This work summarizes the current state of IMI in thoracic surgical oncology, highlighting key translational studies, established and emerging clinical applications, and critical limitations within the current landscape. The authors also outline future directions for the field, including quantitative fluorescence interpretation, standardized reporting, and outcomes-driven clinical trials evaluating margin adequacy, recurrence, staging impact, and cost-effectiveness to support widespread evidence-based implementation. Full article

Necrotizing enterocolitis (NEC) remains one of the most serious gastrointestinal emergencies in preterm infants, and imaging plays a central role in diagnosis and clinical management. Historically, evaluation has relied primarily on abdominal radiography, which remains widely available and embedded in established diagnostic frameworks. However, the hallmark radiographic signs of NEC (i.e., pneumatosis intestinalis, portal venous gas, and free air) reflect relatively advanced manifestations of intestinal injury that indicate established mucosal disruption or transmural necrosis. Bowel ultrasound has increasingly complemented radiography by enabling real-time assessment of bowel wall integrity, perfusion, motility, and intra-abdominal fluid, providing physiologic information that may refine clinical interpretation and monitoring of disease progression. Expanding use of neonatologist-performed bowel ultrasound may further improve access to bedside intestinal imaging and facilitate more timely evaluation in neonatal intensive care settings. In parallel, emerging imaging technologies seek to extend the capabilities of conventional imaging by interrogating biologic processes that underlie intestinal injury. Modalities such as contrast-enhanced ultrasound, ultra-high-frequency ultrasound, and photoacoustic imaging offer the potential to characterize bowel microvascular perfusion, tissue oxygenation, and microstructural changes that may precede overt radiographic abnormalities. Complementary physiologic monitoring approaches are also being explored to identify infants at risk before clinical disease develops. Techniques including superior mesenteric artery Doppler, near-infrared spectroscopy, bowel acoustic monitoring, and electrogastrography aim to detect early alterations in intestinal perfusion, oxygenation, and motility. In addition, artificial intelligence applied to imaging and physiologic data may enhance pattern recognition, risk stratification, and clinical decision support. Together, these advances suggest that NEC evaluation is evolving from a paradigm focused on detecting late structural injury toward integrated approaches capable of identifying intestinal vulnerability earlier and monitoring disease more precisely. Full article

The use of near-infrared (NIR) fluorescence imaging has become increasingly widespread in the intraoperative management of human cancer patients, with diverse applications that enhance the visualization of tumors, metastatic lesions, draining lymph nodes, and critical anatomy. In recent years, the adoption of NIR imaging in small animal surgical oncology has grown, with reported applications including nonselective and selective tumor imaging as well as sentinel lymph node (SLN) mapping for staging and surgical guidance. Despite these advances, clinical use in veterinary patients remains in its early stages, and further work is needed to define indications, optimize protocols, and determine its impact on outcomes. This review summarizes the current applications of NIR imaging in companion animal oncologic surgery, including techniques for dye administration, imaging agents, and reported clinical uses across tumor types. Key limitations are discussed, including limited tissue penetration, lack of tumor specificity with commonly used agents such as indocyanine green, variability in protocols, and limited data in certain species. Emerging technologies, including targeted fluorescent agents and advanced imaging approaches, are also highlighted. Overall, NIR imaging represents a promising adjunct in small animal surgical oncology, though further study and standardization are needed to support broader clinical integration. Full article

Crop attribute detection, as a key component of intelligent agricultural harvesting machinery, plays a crucial role in harvesting efficiency, loss reduction, and autonomous operation control. Compared with existing reviews on artificial intelligence and sensing technologies in agriculture, this review focuses on crop attribute detection scenarios oriented toward the intelligent decision-making and control requirements of agricultural harvesting machinery. It mainly analyzes crop attributes that affect harvesting operations, as well as the sensors and algorithms involved in detecting these attributes, and further clarifies the relationship between detection methods and control decisions in agricultural harvesting machinery. For grain crops, the key attributes relevant to harvesting operations include plant height, plant density, spike number, crop lodging, canopy structure, and crop position. For fruit and vegetable crops, the key attributes relevant to harvesting operations include maturity, position, and quality. From the perspectives of multi-source data acquisition, data analysis, and attribute detection algorithms, the key technologies in the field of crop attribute detection are systematically summarized and analyzed, including sensors used in crop attribute detection, such as RGB, spectral, near-infrared, and LiDAR sensors, as well as data analysis and recognition approaches, such as image classification, object detection, and point cloud analysis. The complexity of field environments and the dynamics of machine operation are analyzed, highlighting the technical bottlenecks of current detection systems in environmental adaptability, real-time responsiveness, and resistance to interference. To address these challenges, feasible optimization directions were proposed, including multi-sensor fusion, weakly supervised learning, and few-shot learning. This review aims to provide systematic references and theoretical support for the coordinated development of crop detection and control decision-making in intelligent agricultural harvesting systems. Full article

Background and Objectives: Intraoperative assessment of tissue perfusion remains a decisive but imperfect step in abdominal surgery. Surgeons still rely heavily on visual judgement when choosing bowel transection lines, constructing anastomoses, judging intestinal viability, or assessing graft reperfusion, even though these decisions are directly linked to anastomotic leak, conduit ischemia, postoperative liver dysfunction, and graft failure. Hyperspectral imaging (HSI) is an emerging contrast-free optical technology that generates quantitative maps of tissue oxygenation, hemoglobin distribution, water content, and near-infrared perfusion. The present review was designed to evaluate whether clinical intraoperative HSI has matured sufficiently to support a focused systematic review topic in abdominal surgery and to synthesize the currently available human evidence. Methods: A literature search was conducted up to 20 February 2026 using combinations of the terms “hyperspectral imaging”, “HSI”, “abdominal surgery”, “colorectal”, “hepatectomy”, “transplantation”, “pancreatoduodenectomy”, “esophagectomy”, “mesenteric ischemia”, and “intraoperative”. Eligible records were original human clinical studies evaluating intraoperative HSI in abdominal or transplant-related operations with perfusion, oxygenation, or tissue viability as a central endpoint. Review articles, animal studies, non-surgical diagnostic studies, and single-patient case reports were excluded. Data were synthesized narratively because of major heterogeneity in indications, designs, devices, timing of measurements, and reported outcomes. Results: Thirteen studies published between 2019 and 2024 met the eligibility criteria, representing 391 patients. The literature covered colorectal resection, acute mesenteric ischemia, esophageal reconstruction with gastric or colonic conduits, pancreatoduodenectomy, pancreas transplantation, major hepatectomy, liver transplantation, and minimally invasive system validation. Across colorectal studies, HSI frequently demonstrated discordance between visually selected and objectively perfused transection lines, with clinically relevant strategy changes in a substantial proportion of patients. In ischemic and transplant settings, HSI discriminated poorly perfused tissue, identified low near-infrared perfusion values associated with early allograft dysfunction, and quantified reperfusion patterns after clamping or implantation. The evidence base was dominated by prospective single-center feasibility studies with small to moderate sample sizes, and no randomized trials were identified. Conclusions: Clinical intraoperative HSI in abdominal surgery is a genuinely niche yet rapidly expanding topic with a sufficient number of human studies to support a relevant systematic review. Current evidence consistently supports feasibility, quantitative perfusion discrimination, and plausible intraoperative utility, especially in colorectal and transplant-related surgery. However, the field remains methodologically heterogeneous, and the next research priority is multicenter standardization with clinically anchored thresholds and outcome-driven comparative studies. Full article

Remote sensing is widely used in archaeological prospection to detect surface anomalies (crop marks) indicating buried remains, typically through recognition of visual patterns in high- or very high-resolution imagery acquired by means of satellite, airborne, or drone sensors. In contrast, spectroscopic approaches focusing on variations in spectral signatures still remain rarely applied in archaeological research. This study proposes a technological barrier-free method addressed to archaeologists which is based on pixel-level analysis of the Reflectance Values (RV) and spectral shape variations in the visible, near-infrared and short-wave infrared (VIS-NIR-SWIR) range derived from Sentinel-2 imagery. Spectral signatures are extracted through sampling polygons designed to account for the spatial resolution of the different Sentinel-2 bands and their spatial relationship with the location and size of the archaeological features. The RV method is tested on two Roman archaeological contexts: the ancient city of Telesia Vetere (San Salvatore Telesino, Benevento) and a Roman villa at Podere Colle Agnano (Labro, Rieti) using the full Sentinel-2 archive since 2017. While Telesia has previously been investigated through aerial photo interpretation and archaeological fieldwork, the Roman villa at Labro is documented here for the first time. Results show consistent seasonal repeated spectral separability between areas corresponding to known buried archaeological features and surrounding areas. Similar anomalies were also detected in areas without previously documented remains, thus suggesting the possible presence of buried structures and highlighting the predictive potential of the RV method. Owing to its easiness to use beyond image processing specialism and reliance on open-access data, the method can support archaeological decision-making and guide further investigation with higher-resolution remote sensing data or targeted field surveys, particularly in the framework of preventive archaeology. Full article

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems. Full article

Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata. The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R²) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R² values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata, providing an efficient technical approach for quality detection of edible and medicinal crops. Full article

Glioblastoma (GBM) remains the most aggressive primary malignant brain tumor in adults, with poor survival largely driven by diffuse cellular infiltration, profound heterogeneity, and near-universal recurrence following standard therapy. Although maximizing the extent of resection is a key determinant of patient outcome, current clinical imaging modalities lack the spatial resolution necessary to detect microscopic tumor invasion and therapy-resistant cell populations. Emerging in vivo imaging technologies capable of cellular and near-single-cell resolution have therefore become a major focus in preclinical neuro-oncology research, with growing relevance for surgical guidance, treatment adaptation, and translational discovery. This review evaluates multiple optical imaging modalities, including multi-photon microscopy, near-infrared II fluorescence imaging, bioluminescence imaging, photoacoustic imaging, optical coherence tomography, confocal laser endomicroscopy, Raman spectroscopy, autofluorescence microscopy, and fluorescence macroscopy with a focus on their ability to detect residual GBM cells. Despite significant advances, these approaches remain constrained by limitations in molecular target availability, probe delivery across the blood–brain barrier, and signal variability within heterogeneous tumor regions. The biological complexity of GBM further challenges detection, as residual tumor cells are spatially dispersed and phenotypically diverse, limiting the effectiveness of single-marker or single-modality strategies. Together, these findings highlight the need for integrated, biologically informed imaging approaches to improve detection of residual disease and guide surgical decision making. Full article

Zygomatic implant perforated (ZIP) flap reconstruction offers immediate surgical rehabilitation following maxillectomy, integrating oncologic zygomatic implants with a fascio-cutaneous free flap. A critical technical challenge is safely perforating the free flap skin paddle to accommodate implants’ abutments without damaging its vasculature. Near-infrared (NIR) vein visualization technology provides real-time mapping of subcutaneous vessels and has been widely investigated in settings such as pediatric intravenous (IV) cannulation. By projecting vein pathways onto the skin, NIR visualization facilitates precise vascular identification, potentially reducing complications. We describe a case of ZIP flap reconstruction in a 25-year-old patient utilizing NIR vein visualization to preemptively locate flap vasculature and minimize the risk of vessel puncture. Our discussion places these findings within the context of the existing literature on NIR devices, underscoring their benefits of non-invasive operation, rapid imaging, and minimal need for advanced operator skills, and highlighting their utility in microvascular reconstructive surgery. Full article

Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red–Green–Blue (RGB) and red–green–blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing. Full article

Light-based diagnostic and therapeutic approaches are increasingly important in modern biomedicine, with organic dyes emerging as versatile optical agents due to their tunable photophysical properties. Precise control over absorption and emission characteristics has enabled their application in fluorescence, photoacoustic, and Raman imaging, as well as in photodynamic and photothermal therapies. However, challenges related to biocompatibility, aqueous stability, and in vivo performance remain critical for clinical translation. Organic dyes that absorb in the near-infrared region are particularly attractive because of their deeper tissue penetration and reduced background interference. This review highlights key structure property relationships of organic dyes and summarizes current design strategies, including chromophore modification, peripheral functionalization for water solubility, and self-assembled nanotheranostic systems. Recent biomedical applications in cancer diagnosis and therapy, bacterial detection, and imaging-guided treatment are discussed, along with future directions for advancing dye-based technologies in healthcare. Full article