Search Results (463)

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

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician’s decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as ‘black boxes’ that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications. Full article

Live-cell imaging generally requires pretreatment with fluorophores to either monitor cellular functions or the dynamics of intracellular processes and structures. We have recently introduced full-field optical coherence tomography for the label-free live-cell imaging of fungi with potential clinical applications for the diagnosis of invasive fungal mold infections. While both the spatial resolution and technical set up of this technology are more likely designed for the histopathological analysis of tissue biopsies, there is to our knowledge no previous work reporting the use of a light interference-based optical technique for direct mycological examination and monitoring of intracellular processes. We describe the first application of dynamic full-field optical transmission tomography (D-FF-OTT) to achieve both high-resolution and live-cell imaging of fungi. First, D-FF-OTT allowed for the precise examination and identification of several elementary structures within a selection of fungal species commonly known to be responsible for invasive fungal infections such as Candida albicans, Aspergillus fumigatus, or Rhizopus arrhizus. Furthermore, D-FF-OTT revealed the intracellular trafficking of organelles and vesicles related to metabolic processes of living fungi, thus opening new perspectives in fast fungal infection diagnostics. Full article

Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique. Full article

Background/Objectives: High-grade gliomas are the most common primary malignant brain tumors in adults. These neoplasms remain predominantly incurable due to the genetic diversity within each tumor, leading to varied responses to specific drug therapies. With the advent of new targeted and immune therapies, which have demonstrated promising outcomes in clinical trials, there is a growing need for image-based techniques to enable early prediction of treatment response. This study aimed to evaluate the potential of radiomics and artificial intelligence implementation in predicting progression-free survival (PFS) in patients with highest-grade glioma (CNS WHO 4) undergoing a standard treatment plan. Methods: In this retrospective study, prediction models were developed in a cohort of 51 patients with pathologically confirmed highest-grade glioma (CNS WHO 4) from the authors’ institution and the repository of the Cancer Imaging Archive (TCIA). Only patients with confirmed recurrence after complete tumor resection with adjuvant radiotherapy and chemotherapy with temozolomide were included. For each patient, 109 radiomic features of the tumor were obtained from a preoperative magnetic resonance imaging (MRI) examination. Four clinical features were added manually—sex, weight, age at the time of diagnosis, and the lobe of the brain where the tumor was located. The data label was the time to recurrence, which was determined based on follow-up MRI scans. Artificial intelligence algorithms were built to predict PFS in the training set (n = 75%) and then validate it in the test set (n = 25%). The performance of each model in both the training and test datasets was assessed using mean absolute percentage error (MAPE). Results: In the test set, the random forest model showed the highest predictive performance with 1-MAPE = 92.27% and a C-index of 0.9544. The decision tree, gradient booster, and artificial neural network models showed slightly lower effectiveness with 1-MAPE of 88.31%, 80.21%, and 91.29%, respectively. Conclusions: Four of the six models built gave satisfactory results. These results show that artificial intelligence models combined with radiomic features could be useful for predicting the progression-free survival of high-grade glioma patients. This could be beneficial for risk stratification of patients, enhancing the potential for personalized treatment plans and improving overall survival. Further investigation is necessary with an expanded sample size and external multicenter validation. Full article

The World Health Organization (WHO) cancer agency predicts that more than 35 million cases of cancer will be experienced in 2050, a 77% increase over the 2022 estimate. Currently, the main cancers diagnosed are breast, lung, and colorectal. There is no standardized tool for cancer diagnoses; initially, clinical procedures are guided by the patient symptoms and usually involve biochemical blood tests, imaging, and biopsy. Label-free non-linear optical approaches are promising tools for tumor imaging, due to their inherent non-invasive biosafe contrast mechanisms and the ability to monitor collagen-related disorders, and biochemical and metabolic changes during cancer progression. In this review, the main non-linear microscopy techniques are discussed, according to three main contrast mechanisms: biochemical, metabolic, and structural imaging. Full article

The geometric shape, symmetry, and topology of colloidal particles often allow for controlling colloidal phase behavior and physical properties of these soft matter systems. In liquid crystalline dispersions, colloidal particles with low symmetry and nontrivial topology of surface confinement are of particular interest, including surfaces shaped as handlebodies, spirals, knots, multi-component links, and so on. These types of colloidal surfaces induce topologically nontrivial three-dimensional director field configurations and topological defects. Director switching by electric fields, laser tweezing of defects, and local photo-thermal melting of the liquid crystal host medium promote transformations among many stable and metastable particle-induced director configurations that can be revealed by means of direct label-free three-dimensional nonlinear optical imaging. The interplay between topologies of colloidal surfaces, director fields, and defects is found to show a number of unexpected features, such as knotting and linking of line defects, often uniquely arising from the nonpolar nature of the nematic director field. This review article highlights fascinating examples of new physical behavior arising from the interplay of nematic molecular order and both chiral symmetry and topology of colloidal inclusions within the nematic host. Furthermore, the article concludes with a brief discussion of how these findings may lay the groundwork for new types of topology-dictated self-assembly in soft condensed matter leading to novel mesostructured composite materials, as well as for experimental insights into the pure-math aspects of low-dimensional topology. Full article

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming. Full article

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net (‘bead’) architectures (termed ‘Θ-Net’ in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology). Full article

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an $F_1$-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. Full article

Photoacoustic imaging (PAI) is an emerging noninvasive and label-free method for capturing the vasculature, hemodynamics, and physiological responses following drug delivery. PAI combines the advantages of optical and acoustic imaging to provide high-resolution images with multiparametric information. In recent decades, PAI’s abilities have been used to determine reactivity after the administration of various drugs. This study investigates photoacoustic imaging as a label-free method of monitoring drug delivery responses by observing changes in the vascular system and oxygen saturation levels across various biological tissues. In addition, we discuss photoacoustic studies that monitor the biodistribution and pharmacokinetics of exogenous contrast agents, offering contrast-enhanced imaging of diseased regions. Finally, we demonstrate the crucial role of photoacoustic imaging in understanding drug delivery mechanisms and treatment processes. Full article

This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of biological specimens with enhanced clarity and precision. It combines high resolution, enhanced contrast, and quantitative capabilities with non-invasive, label-free imaging. These features make it a useful tool for tissue imaging, providing detailed and accurate insights into tissue structure and dynamics without compromising the integrity of the samples. Our findings underscore the potential of quantitative phase imaging in histopathology, in the context of automating the process of tissue analysis and diagnosis. Of particular note are the insights gained from the reconstructed phase images, which provide physical data regarding peripheral glandular cell membranes. These observations serve to focus attention on pathologies involving the basal membrane, such as early invasive carcinoma. Through our analysis, we aim to contribute to catalyzing further advancements in tissue (breast and colon) imaging. Full article

Here, we report a rapid and accurate optical method for detecting cells from liquid samples in a label-free manner. The working principle of the method is based on the interference of parts of a conical laser beam, coming from a single-mode optical fiber directly, and reflected from a flat glass surface. The glass is functionalized by antibodies against the cells to be detected from the liquid sample. Cells bound to that surface modify the reflected beam, and hence, change the resulting interference pattern, too. By registering and interpreting the variation in the image, the presence of cells from the sample can be detected. As for a demonstration, cell suspensions from a U937 cell line were used in glass chambers functionalized by antibodies (TMG6-5 (mIgG1)) to which the cells specifically bind. The limit of detection (LOD) of the method was also estimated. This proof-of-concept setup offers a cost-effective and easy-to-use way of rapid and specific detection of any type of cells (including pathogens) from suspensions (e.g., body fluids). The possible portability of the device predicts its applicability as a rapid test in clinical diagnostics. Full article