Search Results (475)

Background/Objectives: Autogenous palatal free gingival graft (FGG) harvesting presents challenges for patients due to the increased risk of postoperative morbidity related to a second intraoral surgical wound that heals with secondary intention. This parallel-group, randomized, controlled, open-label trial aimed to evaluate the efficacy of the application of leukocyte- and platelet-rich fibrin (L-PRF) membrane to the palatal donor site on wound healing, hemostasis, and pain control after FGG harvesting. Methods: Twenty-eight adult patients with insufficient attached gingiva underwent soft tissue augmentation using FGG harvested from the palate at the Department of Oral and Maxillofacial Surgery, Baskent University, Turkey. Patients were randomized to either an L-PRF group or a control group. In the L-PRF group, the L-PRF membrane was sutured to the donor sites, whereas in the control group, donor sites healed by secondary intention. Postoperative evaluations were conducted on days 1, 3, 5, and 7 and at weeks 2, 3, 4, 5, and 6. Donor sites were evaluated clinically for pain, burning sensation, bleeding, wound healing, and color match to adjacent tissues. Donor site wound areas were analyzed using digital images. Results: Two patients were excluded from the analysis due to loss of contact, leaving 26 (n = 13, n = 13) patients for analysis. Donor site pain and burning sensation were significantly lower in the L-PRF group compared to the control group during the first two postoperative weeks (p < 0.001). Bleeding was significantly lower in the L-PRF group on postoperative days 1 and 3 (p < 0.001). Clinical healing index scores were significantly higher in the L-PRF group at weeks 3 and 4 (p < 0.001). Additionally, palatal wound area reductions from baseline were significantly greater in the L-PRF group at all follow-up intervals (p < 0.001). Conclusions: The application of an L-PRF membrane to palatal donor wounds after FGG harvesting significantly reduces postoperative pain, decreases bleeding, and accelerates healing, providing a valuable autologous biomaterial for enhanced wound healing and improved patient comfort. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article

Hematological analysis is crucial for diagnosing and monitoring blood-related disorders. Nevertheless, conventional hematology analyzers remain confined to laboratory settings due to their high cost, substantial space requirements, and maintenance needs. Herein, we present a portable cell tracking velocimetry (CTV) device for the precise measurement of the magnetic susceptibility of biological entities at the single-cell level, focusing on red blood cells (RBCs) in this work. The system integrates a microfluidic channel positioned between permanent magnets that generate a well-defined magnetic field gradient (191.82 TA/mm²). When the cells are injected into the chamber, their particular response to the magnetic field is recorded and used to estimate their properties and quantify their intracellular hemoglobin (Hb) concentration. We successfully track over 400 RBCs per condition using imaging and trajectory analysis, enabling detailed characterizations of their physical and magnetic properties. A comparison of the mean corpuscular hemoglobin measurements revealed a strong correlation between our CTV system and standard ultraviolet–visible (UV-Vis) spectrophotometry (23.1 ± 5.8 pg vs. 22.4 ± 3.9 pg, p > 0.05), validating the accuracy of our measurements. The system’s single-cell resolution reveals population distributions unobtainable through conventional bulk analysis methods. Thus, this portable CTV technology provides a rapid, label-free approach for magnetic cell characterization, offering new possibilities for point-of-care hematological analysis and field-based research applications. Full article

In amyloid brain PET, after parcellation using the finite element method (FEM)-based algorithm FreeSurfer and voxel-based algorithm PMOD, SUVr examples can be extracted and compared. This study presents the classification SUVr threshold in PET images of F-18 florbetaben (FBB), F-18 flutemetamol (FMM), and F-18 florapronol (FPN) and compares and analyzes the classification performance according to computational algorithm in each brain region. PET images were co-registered after the generated MRI was registered with standard template information. Using MATLAB script, SUVr was calculated using the built-in parcellation number labeled in the brain region. PMOD and FreeSurfer with different algorithms were used to load the PET image, and after registration in MRI, it was normalized to the MRI template. The volume and SUVr of the individual gray matter space region were calculated using an automated anatomical labeling atlas. The SUVr values of eight regions of the frontal cortex (FC), lateral temporal cortex (LTC), mesial temporal cortex (MTC), parietal cortex (PC), occipital cortex (OC), anterior and posterior cingulate cortex (GCA, GCP), and composite were calculated. After calculating the correlation of SUVr using the FreeSurfer and PMOD algorithms and calculating the AUC for amyloid-positive/negative subjects, the classification ability was calculated, and the SVUr threshold was calculated using the Youden index. The correlation coefficients of FreeSurfer and PMOD SUVr calculations of the eight regions of the brain cortex were FBB (0.95), FMM (0.94), and FPN (0.91). The SUVr threshold was SUVr(LTC,min) = 1.264 and SUVr(THA,max) = 1.725 when calculated using FPN-FreeSurfer, and SUVr(MTC,min) = 1.093 and SUVr(MCT,max) = 1.564 when calculated using FPN-PMOD. The AUC comparison showed that there was no statistically significant difference (p > 0.05) in the SUVr classification results using the three radiopharmaceuticals, specifically for the LTC and OC regions in the PMOD analysis, and the LTC and PC regions in the FreeSurfer analysis. The SUVr calculation using PMOD (voxel-based algorithm) has a strong correlation with the calculation using FreeSurfer (FEM-based algorithm); therefore, they complement each other. Quantitative classification analysis with high accuracy is possible using the suggested SUVr threshold. The SUVr classification performance was good in the order of FMM, FBB, and FPN, and showed a good classification performance in the LTC region regardless of the type of radiotracer and analysis algorithm. Full article

Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research department is developing the tauCAM^TM, based on the Current-Assisted Photonic Sampler, to achieve real-time fluorescence lifetime imaging in the NIR (700–900 nm) region. Incorporating fluorescence lifetime into endoscopy could further improve the differentiation of malignant and benign cells based on their distinct lifetimes. In this work, the capabilities of an endoscopic lifetime imaging system are demonstrated using a rigid endoscope involving various phantoms and an IRF-free deep learning-based method with only 6-time points. The results show that this application’s fluorescence lifetime image has better lifetime uniformity and precision with 6-time points than the conventional methods. Full article

Background: The wide variability in clinical responses to anti-tumor immunotherapy drives the search for personalized strategies. One of the promising approaches is drug screening using patient-derived models composed of tumor and immune cells. In this regard, the selection of an appropriate in vitro model and the choice of cellular response assay are critical for reliable predictions. Fluorescence lifetime imaging microscopy (FLIM) is a powerful, non-destructive tool that enables direct monitoring of cellular metabolism on a label-free basis with a potential to resolve metabolic rearrangements in immune cells associated with their reactivity. Objective: The aim of the study was to develop a patient-derived glioma explant model enriched by autologous peripheral lymphocytes and explore FLIM of the redox-cofactor NAD(P)H in living lymphocytes to measure the responses of the model to immune checkpoint inhibitors. Methods: The light microscopy, FLIM of NAD(P)H and flow cytometry were used. Results: The results demonstrate that the responsive models displayed a significant increase in the free NAD(P)H fraction α₁ after treatment, associated with a shift towards glycolysis due to lymphocyte activation. The non-responsive models exhibited no alterations or a decrease in the NAD(P)H α₁ after treatment. The FLIM data correlated well with the standard assays of immunotherapy drug response in vitro, including morphological changes, the T-cells activation marker CD69, and the tumor cell proliferation index Ki67. Conclusions: The proposed platform that includes tumor explants co-cultured with lymphocytes and the NAD(P)H FLIM assay represents a promising solution for the patient-specific immunotherapeutic drug screening. Full article

Achieving rapid and effective object detection in large-scale unmanned aerial vehicle (UAV) images presents a challenge. Existing methods typically split the original large UAV image into overlapping patches and perform object detection on each image patch. However, the extensive object-free background areas in large-scale aerial imagery reduce detection efficiency. To address this issue, we propose an efficient object detection approach for large-scale UAV aerial imagery via multi-task classification. Specifically, we develop a lightweight multi-task classification (MTC) network to efficiently identify background areas. Our method leverages bounding box label information to construct a salient region generation branch. Then, to improve the training process of the classification network, we design a multi-task loss function to optimize the parameters of the multi-branch network. Furthermore, we introduce an optimal classification threshold strategy to balance detection speed and accuracy. Our proposed MTC network can rapidly and accurately determine whether an aerial image patch contains objects, and it can be seamlessly integrated with existing detectors without the need for retraining. We conduct experiments on three datasets to verify the effectiveness and efficiency of our classification-driven detection method, including the DOTA v1.0, DOTA v2.0, and ASDD datasets. In the large-scale UAV images and ASDD dataset, our proposed method increases the detection speed by more than 30% and 130%, respectively, while maintaining good object detection performance. Full article

Background/Objectives: Cancer cells rely on metabolic reprogramming that is supported by altered mitochondrial redox status and an increased demand for NAD⁺. Over expression of Nampt, the rate-limiting enzyme of the NAD⁺ biosynthesis salvage pathway, is common in breast cancer cells, and more so in triple negative breast cancer (TNBC) cells. Targeting the salvage pathway has been pursued for cancer therapy. However, TNBC cells have heterogeneous responses to Nampt inhibition, which contributes to the diverse outcomes. There is a lack of imaging biomarkers to differentiate among TNBC cells under metabolic stress and identify which are responsive. We aimed to characterize and differentiate among a panel of TNBC cell lines under NAD-deficient stress and identify which subtypes are more dependent on the NAD salvage pathway. Methods: Optical redox imaging (ORI), a label-free live cell imaging microscopy technique was utilized to acquire intrinsic fluorescence intensities of NADH and FAD-containing flavoproteins (Fp) thus the mitochondrial redox ratio Fp/(NADH + Fp) in a panel of TNBC cell lines. Various fluorescence probes were then added to the cultures to image the mitochondrial ROS, mitochondrial membrane potential, mitochondrial mass, and cell number. Results: Various TNBC subtypes are sensitive to Nampt inhibition in a dose- and time-dependent manner, they have differential mitochondrial redox responses; furthermore, the mitochondrial redox indices linearly correlated with mitochondrial ROS induced by various doses of a Nampt inhibitor. Moreover, the changes in the redox indices correlated with growth inhibition. Additionally, the redox state was found fully recovered after removing the Nampt inhibitor. Conclusions: This study supports the utility of ORI in rapid metabolic phenotyping of TNBC cells under NAD-deficient stress to identify responsive cells and biomarkers of treatment response, facilitating combination therapy strategies. Full article

Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures. Traditional enhancement techniques like Fourier transform filtering and spectral unmixing require extensive preprocessing and often introduce artifacts. More recently, deep learning techniques, which have shown great promise in enhancing image quality, face their own limitations. Specifically, conventional deep learning models require large quantities of high-quality, manually labeled training data for effective denoising and super-resolution tasks, which is challenging to obtain in multi-modal microscopy. In this study, we address these limitations by exploring advanced zero-shot and self-supervised learning approaches, such as ZS-DeconvNet, Noise2Noise, Noise2Void, Deep Image Prior (DIP), and Self2Self, which enhance image quality without the need for labeled and large datasets. This study offers a comparative evaluation of zero-shot and self-supervised learning methods, evaluating their effectiveness in denoising, resolution enhancement, and preserving biological structures in multi-modal Raman light sheet microscopic images. Our results demonstrate significant improvements in image clarity, offering a reliable solution for visualizing complex biological systems. These methods establish the way for future advancements in high-resolution imaging, with broad potential for enhancing biomedical research and discovery. Full article

Background: Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples. Methods: In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at −80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs. Results: The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1–5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53). Conclusions: Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools. Full article

Organ-on-a-chip (OOC) devices mimic human organs, which can be used for many different applications, including drug development, environmental toxicology, disease models, and physiological assessment. Image data acquisition and analysis from these chips are crucial for advancing research in the field. In this study, we propose a label-free morphology imaging platform compatible with the small airway-on-a-chip system. By integrating deep learning and image recognition techniques, we aim to analyze the differentiability of human small airway epithelial cells (HSAECs). Utilizing cell imaging on day 3 of culture, our approach accurately predicts the differentiability of HSAECs after 4 weeks of incubation. This breakthrough significantly enhances the efficiency and stability of establishing small airway-on-a-chip models. To further enhance our analysis capabilities, we have developed a customized MATLAB program capable of automatically processing ciliated cell beating images and calculating the beating frequency. This program enables continuous monitoring of ciliary beating activity. Additionally, we have introduced an automated fluorescent particle tracking system to evaluate the integrity of mucociliary clearance and validate the accuracy of our deep learning predictions. The integration of deep learning, label-free imaging, and advanced image analysis techniques represents a significant advancement in the fields of drug testing and physiological assessment. This innovative approach offers unprecedented insights into the functioning of the small airway epithelium, empowering researchers with a powerful tool to study respiratory physiology and develop targeted interventions. Full article

Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the quality of clinical decisions. Raman spectroscopy, with its high specificity and non-invasive nature, can be an effective tool for dependable and quick histopathology. The most promising modality in this context is stimulated Raman histology (SRH), a label-free, non-linear optical process which generates conventional H&E-like images in short time frames. SRH overcomes limitations of conventional Raman scattering by leveraging the qualities of stimulated Raman scattering (SRS), wherein the energy gets transferred from a high-power pump beam to a probe beam, resulting in high-energy, high-intensity scattering. SRH’s high resolution and non-requirement of preprocessing steps make it particularly suitable when it comes to intrasurgical histology. Combining SRH with artificial intelligence (AI) can lead to greater precision and less reliance on manual interpretation, potentially easing the burden of the overburdened global histopathology workforce. We review the recent applications and advances in SRH and how it is tapping into AI to evolve as a revolutionary tool for rapid histologic analysis. Full article

In recent studies, it has been shown that fluorescence lifetime imaging (FLIM) may reveal intracellular structural details in unstained cytological preparations that are not revealed by standard staining procedures. The aim of our investigation was to examine whether FLIM images could reveal areas suggestive of polymerization in red blood cells (RBCs) of sickle cell disease (SCD) patients. We examined label-free blood films using auto-fluorescence FLIM images of 45 SCD patients and compared the results with those of 27 control persons without hematological disease. All control RBCs revealed homogeneous cytoplasm without any foci. Rounded non-sickled RBCs in SCD showed between zero and three small intensively fluorescent dots with higher lifetime values. In sickled RBCs, we found additionally larger irregularly shaped intensively fluorescent areas with increased FLIM values. These areas were interpreted as equivalent to polymerized hemoglobin. The rounded, non-sickled RBCs of SCD patients with homogeneous cytoplasm were not different from those of the erythrocytes of control patients in light microscopy. Yet, variables from the local binary pattern-transformed matrix of the FLIM values per pixel showed significant differences between non-sickled RBCs and those of control cells. In a linear discriminant analysis, using local binary pattern-transformed texture features (mean and entropy) of the erythrocyte cytoplasm of normal appearing cells, the final model could distinguish between SCD patients and control persons with an accuracy of 84.7% of the patients. When the classification was based on the examination of a single rounded erythrocyte, an accuracy of 68.5% was achieved. Employing the Linear Discriminant Analysis classifier method for machine learning, the accuracy was 68.1%. We believe that our study shows that FLIM is able to disclose the topography of the intracellular polymerization process of hemoglobin in sickle cell disease and that the images are compatible with the theory of the two-step nucleation. Furthermore, we think that the presented technique may be an interesting tool for the investigation of therapeutic inhibition of polymerization. Full article

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents. Full article

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields. Full article