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Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer.Significance:An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. Significance: An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Prostate cancer treatment decisions are heavily reliant upon the subjective visual interpretation (assigning Gleason patterns, or ISUP grade groups) of limited numbers of 2D histology sections. Under this paradigm, inter-observer variance is high, with ISUP grades not correlating well with the outcomes of individual patients, which contributes to the over- and under-treatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of 3D glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g., nuclear size, sphericity). 3D pathology datasets were generated with open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of n = 46 patients. A deep learning-based segmentation workflow for 3D nuclear segmentation and 3D glandular segmentation was used to segment nuclei within the glandular epithelium vs. stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (AUC = 0.72 vs. 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with risk of BCR than analogous 2D features (AUC = 0.72 vs. 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. In current clinical practice, the microscopic architecture of the prostate glands is what forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a limited number of 2D histology sections is often unreliable, which contributes to the under- and over-treatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for non-destructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analog of standard H&E staining. Our analysis is based on interpretable glandular features, and is facilitated by the development of image-translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep-learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring real immunolabeling. To provide evidence of the translational value of a computational 3D pathology approach, we analyzed ex vivo biopsies (n = 300) extracted from archived radical-prostatectomy specimens (N = 50), and found that 3D glandular features are superior to corresponding 2D features for risk stratification of low-to intermediate-risk PCa patients based on their clinical biochemical recurrence (BCR) outcomes.SignificanceWe present an end-to-end pipeline for computational 3D pathology of whole prostate biopsies, showing that non-destructive pathology has the potential to enable superior prognostic stratification for guiding critical oncology decisions.

Recent advances in optical clearing and light-sheet microscopy have provided unprecedented access to structural and molecular information from intact tissues. However, current light-sheet microscopes have imposed constraints on the size, shape, number of specimens, and compatibility with various clearing protocols. Here we present a multi-immersion open-top light-sheet microscope that enables simple mounting of multiple specimens processed with a variety of clearing protocols, which will facilitate wide adoption by preclinical researchers and clinical laboratories. In particular, the open-top geometry provides unsurpassed versatility to interface with a wide range of accessory technologies in the future., Light-sheet microscopes are increasingly used for imaging cleared tissues, but have imposed constraints on sample geometries and protocols. Here the authors present a multi-immersion open-top light-sheet microscope to overcome these limitations and enable high-throughput imaging of samples processed with various clearing protocols.

We demonstrate theoretically and empirically that financial development of the destination country is as important as that of the origin country in shaping bilateral trade patterns, on both the extensive margin and the intensive margin of international trade.

Glandular architecture is currently the basis for the Gleason grading of prostate biopsies. To visualize and computationally analyze glandular features in large 3D pathology datasets, we developed an annotation-free segmentation method for 3D prostate glands that relies upon synthetic 3D immunofluorescence (IF) enabled by generative adversarial networks. By using a fluorescent analog of H and E (cheap and fast stain) as an input, our strategy allows for accurate glandular segmentation that does not rely upon subjective and tedious human annotations or slow and expensive 3D immunolabeling. We aim to demonstrate that this 3D segmentation will enable improved prostate cancer prognostication.

A current challenge is providing an accurate diagnosis in a timely manner for patients at risk of having prostate cancer. We developed and demonstrated a non-destructive procedure in which 12 biopsies can be cleared, fluorescently labeled, imaged with an open-top light-sheet (OTLS) microscope, and then diagnosed by a pathologist within an hour of biopsy. Using conventional histology as the gold standard, the accuracy, sensitivity, and specificity of 1Hr2Dx were all >90%. Such a method could potentially provide patients with a preliminary on-site diagnosis after a biopsy procedure, thereby alleviating anxiety and potentially expediting treatments.

Fluorescence-based slide-free digital pathology techniques, including 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. Since pathologists are accustomed to the appearance of standard histology stains, the ability to render grayscale fluorescent images with color palettes that mimic traditional histology is valuable. We present FalseColor-Python, an open-source rapid digital-staining package that renders two-channel fluorescence images to mimic standard histology. Our package offers consistent color-space representations that are robust to both intra-specimen and inter-specimen variations in staining and imaging conditions, along with GPU-accelerated methods to process large datasets efficiently.

What do small firms do when given an income tax cut? We address this question by examining the consequences of a sharp reduction in the corporate income tax rate for small- and micro-profit enterprises (SMPE) in China based on confidential tax returns. Utilizing the gradual increases in the qualifying threshold for SMPEs during 2010-2016, we find that newly qualified SMPEs with positive taxable income increased investment, interest expense and productivity. SMPEs in taxable losses did not respond to the tax cut. The tax cut induced more SMPEs to register, especially those in financially constrained sectors. Despite these positive effects, firms’ fixed asset growth slows down when they get closer to the SMPE threshold. Our study contributes to understanding the effect of tax preferences for small businesses.

Significance: Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient’s quality of life. Aim: We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies. Approach: We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology. Results: The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both >90%. Conclusions: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

An ~45-minute workflow, consisting of fluorescence labeling, tissue clearing, and 3D non-destructive, open-top light-sheet (OTLS) microscopy, has been developed for diagnosis of 12 human prostate needle cores within an hour after biopsy.

The development of novel photosensitizers with aggregation-induced emission (AIE) characteristics has aroused tremendous interest, because it could combine efficient bioimaging with precise photodynamic therapy, which would thus dramatically promote applications in biomedical treatment. Among various AIE luminogens (AIEgens), heterocycle-containing molecules are highly promising for this usage because of their high photostability and tunable electronic properties. In this work, a pyrazine-containing AIEgen with a dicyanopyrazine moiety as an electron acceptor and a triphenylamine unit as an electron donor was chosen for study. The V-shaped donor-π-acceptor-π-donor structure of the AIEgen endowed its nanoparticles with excellent nonlinear optical properties for two-photon cell imaging under near-infrared laser excitation. Also, under the same excitation, the nanoparticles could produce reactive oxygen species and further kill the cells efficiently and accurately. The present work thus presents a pyrazine-containing AIEgen as a new photosensitizer for imaging-guided two-photon photodynamic therapy and gives more opportunities for deep-tissue treatment of cancer in future research.

Rodents are popular biological models for physiological and behavioral research in neuroscience and rats are better models than mice due to their higher genome similarity to human and more accessible surgical procedures. However, rat brain is larger than mice brain and it needs powerful imaging tools to implement better penetration against the scattering of the thicker brain tissue. Three-photon fluorescence microscopy (3PFM) combined with near-infrared (NIR) excitation has great potentials for brain circuits imaging because of its abilities of anti-scattering, deep-tissue imaging, and high signal-to-noise ratio (SNR). In this work, a type of AIE luminogen with red fluorescence was synthesized and encapsulated with Pluronic F-127 to make up form nanoparticles (NPs). Bright DCDPP-2TPA NPs were employed for in vivo three-photon fluorescent laser scanning microscopy of blood vessels in rats brain under 1550[Formula: see text]nm femtosecond laser excitation. A fine three-dimensional (3D) reconstruction up to the deepness of 600[Formula: see text][Formula: see text]m was achieved and the blood flow velocity of a selected vessel was measured in vivo as well. Our 3PFM deep brain imaging method simultaneously recorded the morphology and function of the brain blood vessels in vivo in the rat model. Using this angiography combined with the arsenal of rodent’s brain disease, models can accelerate the neuroscience research and clinical diagnosis of brain disease in the future.

Intraoperative assessment of breast surgical margins will be of value for reducing the rate of re-excision surgeries for lumpectomy patients. While frozen-section histology is used for intraoperative guidance of certain cancers, it provides limited sampling of the margin surface (typically

This study presents thioether construction involving alkyl/aryl thiosulfates and diazonium salt catalyzed by visible-light-excited [Ru(bpy)3 Cl2 ] at room temperature in 44-86 % yield. Electron paramagnetic resonance studies found that thiosulfate radical formation was promoted by K2 CO3 . Conversely, radicals generated from BnSH or BnSSBn (Bn=benzyl) were clearly suppressed, demonstrating the special property of thiosulfate in this system. Transient absorption spectra confirmed the electron-transfer process between [Ru(bpy)3 Cl2 ] and 4-MeO-phenyl diazonium salt, which occurred with a rate constant of 1.69×10(9) M(-1) s(-1) . The corresponding radical trapping product was confirmed by X-ray diffraction. The full reaction mechanism was determined together with emission quenching data. Furthermore, this system efficiently avoided the over-oxidation of sulfide caused by H2 O in the photoexcited system containing Ru(2+) . Both aryl and heteroaryl diazonium salts with various electronic properties were investigated for synthetic compatibility. Both alkyl- and aryl-substituted thiosulfates could be used as substrates. Notably, pharmaceutical derivatives afforded late-stage sulfuration smoothly under mild conditions.

This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-based measures, the newly defined Gcs measure calculates the summation of the gradient correlation between each channel of the color image and the transformed grayscale image. Two efficient algorithms are developed to solve the proposed model. On one hand, due to the highly nonlinear nature of Gcs measure, a solver consisting of the augmented Lagrangian and alternating direction method is adopted to deal with its approximated linear parametric model. The presented algorithm exhibits excellent iterative convergence and attains superior performance. On the other hand, a discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model-induced candidate images. The non-iterative solver has advantages in simplicity and speed with only several simple arithmetic operations, leading to real-time computational speed. In addition, it is very robust with respect to the parameter and candidates. Extensive experiments under a variety of test images and a comprehensive evaluation against existing state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithms.