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1.
Osteoarthr Cartil Open ; 3(1)2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-34386778

RESUMO

Objective: To describe the characteristics of calcium pyrophosphate (CPP) crystal size and morphology under compensated polarized light microscopy (CPLM). Secondarily, to describe CPP crystals seen only with digital enhancement of CPLM images, confirmed with advanced imaging techniques. Methods: Clinical lab-identified CPP-positive synovial fluid samples were collected from 16 joint aspirates. Four raters used a standardized protocol to describe crystal shape, birefringence strength and color. A crystal expert confirmed CPLM-visualized crystal identification. For crystal measurement, a high-pass linear light filter was used to enhance resolution and line discrimination of digital images. This process identified additional enhanced crystals not seen by raters under CPLM. Single-shot computational polarized light microscopy (SCPLM) provided further confirmation of the enhanced crystals' presence. Results: Of 932 suspected crystals identified by CPLM, 569 met our inclusion criteria, and 293 (51%) were confirmed as CPP crystals. Of 175 unique confirmed crystals, 118 (67%) were rods (median area 3.6 µm2 [range, 1.0-22.9 µm2]), and 57 (33%) were rhomboids (median area 4.8 µm2 [range, 0.9-16.7 µm2]). Crystals visualized only after digital image enhancement were smaller and less birefringent than CPLM-identified crystals. Conclusions: CPP crystals that are smaller and weakly birefringent are more difficult to identify. There is likely a population of smaller, less birefringent CPP crystals that routinely goes undetected by CPLM. Describing the characteristics of poorly visible crystals may be of use for future development of novel crystal identification methods.

2.
Nat Commun ; 12(1): 4884, 2021 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-34385460

RESUMO

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost.


Assuntos
Biópsia com Agulha de Grande Calibre/métodos , Aprendizado Profundo , Diagnóstico por Computador/métodos , Nefropatias/patologia , Rim/patologia , Coloração e Rotulagem/métodos , Algoritmos , Corantes/química , Corantes/classificação , Corantes/normas , Diagnóstico Diferencial , Humanos , Nefropatias/diagnóstico , Patologia Clínica/métodos , Patologia Clínica/normas , Padrões de Referência , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Coloração e Rotulagem/normas
3.
Light Sci Appl ; 10(1): 155, 2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34326306

RESUMO

Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.

4.
Health Phys ; 120(3): 321-338, 2021 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-33315649

RESUMO

ABSTRACT: Image reconstruction algorithms were developed for radiation source mapping and used for generating the search path of a moving radiation detector, such as one onboard an unmanned aerial vehicle. Simulations consisted of first assuming radioactive sources of varying complexity and estimating the radiation fields that would then be produced by that source distribution. Next, the "measurements" that would result from a pair of adjacent spatial locations were computed. A crude estimate of the source distribution likely to have produced such "measurements" was reconstructed based upon the limited measurements. Location of the next "measurement" was then determined as halfway between the location of the estimated source and the current "measurement." With each additional sample, improved source distribution reconstructions were made and used to inform the immediate direction of detector motion. Source reconstruction or mapping was formulated as an inverse problem solved with either maximum a posteriori or least squares (LS) regression deconvolution methods. Different amounts of noise were added to the simulated "measurements," allowing evaluation of the methods' performances as functions of signal-to-noise ratio of the measured map. As expected, methods that promote sparsity were better suited in reconstructing point sources. Reliable prior information of the source distribution also improved the reconstruction results, especially with distributed sources. With a non-negative least square algorithm and the suggested paths it generated, location of sources was successfully estimated to an accuracy of 0.014 m within nine iterations in a single-source scenario and 12 iterations in a two-source scenario, given a 10% error on the integrated counts and a Poisson distribution of the noise associated with the measured counts.

5.
IEEE Trans Cybern ; 2020 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-32175883

RESUMO

A well-known problem with distance-based formation control is the existence of multiple equilibrium points not associated with the desired formation. This problem can be potentially mitigated by introducing an additional controlled variable. In this article, we generalize the distance + angle-based scheme for 2-D formations of single-integrator agents by using directed graphs and triangulation of the n-agent formation. We show that under certain conditions on the control gains and desired formation shape, our controller ensures the asymptotic stability of the correct formation for almost all initial agent positions.

6.
J Biophotonics ; 13(1): e201960036, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31483948

RESUMO

Pathological crystal identification is routinely practiced in rheumatology for diagnosing arthritis disease such as gout, and relies on polarized light microscopy as the gold standard method used by medical professionals. Here, we present a single-shot computational polarized light microscopy method that reconstructs the transmittance, retardance and slow-axis orientation of a birefringent sample using a single image captured with a pixelated-polarizer camera. This method is fast, simple-to-operate and compatible with all the existing standard microscopes without extensive or costly modifications. We demonstrated the success of our method by imaging three different types of crystals found in synovial fluid and reconstructed the birefringence information of these samples using a single image, without being affected by the orientation of individual crystals within the sample field-of-view. We believe this technique will provide improved sensitivity, specificity and speed, all at low cost, for clinical diagnosis of crystals found in synovial fluid and other bodily fluids.


Assuntos
Pirofosfato de Cálcio , Gota , Birrefringência , Gota/diagnóstico por imagem , Humanos , Microscopia de Polarização , Líquido Sinovial
7.
ACS Photonics ; 7(11): 3023-3034, 2020 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-34368395

RESUMO

Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs, or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase-recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an inline lensfree holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including, for example, monosodium urate and triamcinolone acetonide crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.

8.
Light Sci Appl ; 8: 91, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31645935

RESUMO

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

9.
J Biophotonics ; 12(11): e201900107, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31309728

RESUMO

We report a framework based on a generative adversarial network that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network learns to eliminate missing-phase-related artifacts, and generates an accurate color transformation for the reconstructed image. Our framework is experimentally demonstrated using lung and prostate tissue sections that are labeled with different histological stains. This framework is envisaged to be applicable to point-of-care histopathology and presents a significant improvement in the throughput of coherent microscopy systems given that only a single hologram of the specimen is required for accurate color imaging.


Assuntos
Aprendizado Profundo , Holografia , Processamento de Imagem Assistida por Computador/métodos , Microscopia , Cor , Humanos , Masculino , Próstata/diagnóstico por imagem
10.
Sci Rep ; 9(1): 3926, 2019 03 08.
Artigo em Inglês | MEDLINE | ID: mdl-30850721

RESUMO

We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. The capabilities of this approach are experimentally validated by super-resolving complex-valued images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.


Assuntos
Aprendizado Profundo , Holografia/métodos , Aumento da Imagem/métodos , Microscopia/métodos , Desenho de Equipamento , Feminino , Holografia/instrumentação , Holografia/estatística & dados numéricos , Humanos , Pulmão/diagnóstico por imagem , Microscopia/instrumentação , Microscopia/estatística & dados numéricos , Redes Neurais de Computação , Teste de Papanicolaou/métodos , Teste de Papanicolaou/estatística & dados numéricos , Software , Esfregaço Vaginal/métodos , Esfregaço Vaginal/estatística & dados numéricos
11.
Light Sci Appl ; 8: 23, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30728961

RESUMO

Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.

12.
BMC Pharmacol Toxicol ; 20(1): 2, 2019 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-30621790

RESUMO

BACKGROUND: The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery. RESULTS: In this work, we describe eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%. CONCLUSIONS: eToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred .


Assuntos
Descoberta de Drogas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Aprendizado de Máquina , Algoritmos , Animais , Humanos
13.
J Biophotonics ; 12(3): e201800335, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30353662

RESUMO

Holographic microscopy presents challenges for color reproduction due to the usage of narrow-band illumination sources, which especially impacts the imaging of stained pathology slides for clinical diagnoses. Here, an accurate color holographic microscopy framework using absorbance spectrum estimation is presented. This method uses multispectral holographic images acquired and reconstructed at a small number (e.g., three to six) of wavelengths, estimates the absorbance spectrum of the sample, and projects it onto a color tristimulus. Using this method, the wavelength selection is optimized to holographically image 25 pathology slide samples with different tissue and stain combinations to significantly reduce color errors in the final reconstructed images. The results can be used as a practical guide for various imaging applications and, in particular, to correct color distortions in holographic imaging of pathology samples spanning different dyes and tissue types.


Assuntos
Holografia , Microscopia , Patologia , Coloração e Rotulagem , Cor , Humanos , Processamento de Imagem Assistida por Computador
14.
Light Sci Appl ; 7: 108, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30564314

RESUMO

Parasitic infections constitute a major global public health issue. Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis. Here, we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. Based on this principle, a cost-effective and mobile instrument, which rapidly screens ~3.2 mL of fluid sample in three dimensions, was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns. We demonstrate the capabilities of our platform by detecting trypanosomes, which are motile protozoan parasites, with various species that cause deadly diseases affecting millions of people worldwide. Using a holographic speckle analysis algorithm combined with deep learning-based classification, we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid (CSF) samples, achieving a limit of detection of ten trypanosomes per mL of whole blood (~five-fold better than the current state-of-the-art parasitological method) and three trypanosomes per mL of CSF. We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis, the causative agent of trichomoniasis, which affects 275 million people worldwide. With its cost-effective, portable design and rapid screening time, this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions.

15.
J Chem Inf Model ; 57(4): 627-631, 2017 04 24.
Artigo em Inglês | MEDLINE | ID: mdl-28346786

RESUMO

Constructing high-quality libraries of molecular building blocks is essential for successful fragment-based drug discovery. In this communication, we describe eMolFrag, a new open-source software to decompose organic compounds into nonredundant fragments retaining molecular connectivity information. Given a collection of molecules, eMolFrag generates a set of unique fragments comprising larger moieties, bricks, and smaller linkers connecting bricks. These building blocks can subsequently be used to construct virtual screening libraries for targeted drug discovery. The robustness and computational performance of eMolFrag is assessed against the Directory of Useful Decoys, Enhanced database conducted in serial and parallel modes with up to 16 computing cores. Further, the application of eMolFrag in de novo drug design is illustrated using the adenosine receptor. eMolFrag is implemented in Python, and it is available as stand-alone software and a web server at www.brylinski.org/emolfrag and https://github.com/liutairan/eMolFrag .


Assuntos
Desenho de Fármacos , Modelos Moleculares , Software , Bases de Dados Factuais , Conformação Molecular , Antagonistas de Receptores Purinérgicos P1/química , Antagonistas de Receptores Purinérgicos P1/farmacologia , Receptores Purinérgicos P1/metabolismo
16.
Phys Rev E Stat Nonlin Soft Matter Phys ; 85(6 Pt 2): 066132, 2012 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-23005187

RESUMO

A chlorine-iodine-malonic-acid Turing system involving a local concentration-dependent diffusivity (LCDD) has fundamental significance for physical, chemical, and biological systems with inhomogeneous medium. We investigated such a system by both numerical computation and mathematical analysis. Our research reveals that a variable local diffusivity has an evident effect on regulating the Turing patterns for different modes. An intrinsic square-root law is given by λ ∼ (c(1)+c(2)k)(1/2), which relates the pattern wavelength (λ) with the LCDD coefficient (k). This law indicates that the system pattern has the properties of an equivalent Turing pattern. The current study confirms that, for the Turing system with LCDD, the system pattern form retains the basic characteristics of a traditional Turing pattern in a wide range of LCDD coefficients.


Assuntos
Cloro/química , Iodo/química , Malonatos/química , Modelos Químicos , Simulação por Computador , Difusão , Cinética
17.
Wei Sheng Yan Jiu ; 35(3): 313-6, 2006 May.
Artigo em Chinês | MEDLINE | ID: mdl-16921757

RESUMO

OBJECTIVE: A oxygen-electrode method is developed for the determination of H2O2 residues in solid food. METHODS: The hydrogen peroxide can be decomposed specifically into water and oxygen by catalase. The oxygen-electrode has high selectivity with the dissolved oxygen (DO). After H2O2 in the samples is extracted by PBS and the system is deoxidized by nitrogen, the DO can be detected by oxygen meter and then the content of H2O2 in samples will be calculated according to the result. RESULTS: The linear range is from 0.5 microg/ml to 7.5 microg/ml r > 0.999. The detection limit is 0.13 microg/ml. The recoveries are between 90.59%-93.88%. BSD is below 10%. CONCLUSION: The method has been used for the detection of H2O2 in health-preservation products of calcium, dried small shrimps, bean curd et al, it proved to be a simple rapid method.


Assuntos
Eletrodos , Análise de Alimentos/métodos , Peróxido de Hidrogênio/análise , Oxigênio/análise , Catalase
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