Cell identity revealed by precise cell cycle state mapping links data modalities.

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

A novel deep learning-based method for automatic stereology of microglia cells from low magnification images.

Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.

An estimation of physiochemical properties of bladder cancer drugs via degree-based chemical bonding topological descriptors.

The use of topological descriptors remains a significant approach due to numerous advances in the field of drug design. Descriptors provide numerical representations of a molecule's chemical characteristics when used with QSPR models. The QSPR analysis for bladder medications is the main focus of this study. Linear regression model is developed for the computed indices values, the physicochemical properties of the bladder medications are examined.Communicated by Ramaswamy H. Sarma.

Mathematical Modeling of Clonal Interference by Density-Dependent Selection in Heterogeneous Cancer Cell Lines.

Many cancer cell lines are aneuploid and heterogeneous, with multiple karyotypes co-existing within the same cell line. Karyotype heterogeneity has been shown to manifest phenotypically, thus affecting how cells respond to drugs or to minor differences in culture media. Knowing how to interpret karyotype heterogeneity phenotypically would give insights into cellular phenotypes before they unfold temporally. Here, we re-analyzed single cell RNA (scRNA) and scDNA sequencing data from eight stomach cancer cell lines by placing gene expression programs into a phenotypic context. Using live cell imaging, we quantified differences in the growth rate and contact inhibition between the eight cell lines and used these differences to prioritize the transcriptomic biomarkers of the growth rate and carrying capacity. Using these biomarkers, we found significant differences in the predicted growth rate or carrying capacity between multiple karyotypes detected within the same cell line. We used these predictions to simulate how the clonal composition of a cell line would change depending on density conditions during in-vitro experiments. Once validated, these models can aid in the design of experiments that steer evolution with density-dependent selection.

Risk of Bleeding after Transcatheter Aortic Valve Replacement: impact of Preoperative Antithrombotic Regimens

Abstract Introduction: Bleeding after transcatheter aortic valve replacement (TAVR) has a negative impact on the outcome of the procedure. Risk factors for bleeding vary widely in the literature, and the impact of preoperative antithrombotic agents has not been fully established. The objectives of our study were to assess bleeding after TAVR as defined by the Valve Academic Research Consortium-2 (VARC-2), identify its risk factors, and correlate with antithrombotic treatment in addition to its effect on procedural mortality. Methods: The study included 374 patients who underwent TAVR from 2009 to 2018. We grouped the patients into four groups according to the VARC-2 definition of bleeding. Group 1 included patients without bleeding (n=265), group 2 with minor bleeding (n=22), group 3 with major bleeding (n=61), and group 4 with life-threatening bleeding (n=26). The median age was 78 (25th-75th percentiles: 71-82), and 226 (60.4%) were male. The median EuroSCORE was 3.4 (2-6.3), and there was no difference among groups (P=0.886). The TAVR approach was transfemoral (90.9%), transapical (5.6%), and trans-subclavian (1.9%). Results: Predictors of bleeding were stroke (OR: 2.465; P=0.024) and kidney failure (OR: 2.060; P=0.046). Preoperative single and dual antiplatelet therapy did not increase the risk of bleeding (P=0.163 and 0.1, respectively). Thirty-day mortality occurred in 14 patients (3.7%), and was significantly higher in patients with life-threatening bleeding (n=8 [30.8%]; P<0.001). Conclusion: Bleeding after TAVR is common and can be predicted based on preprocedural comorbidities. Preprocedural antithrombotic therapy did not affect bleeding after TAVR in our population.

A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting.

The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate stereological quantification of microscopic structures in stained tissue sections plays a critical role in understanding human diseases and developing safe and effective treatments. In this article, we review the most recent deep learning approaches for cell (nuclei) detection and segmentation in cancer and Alzheimer's disease with an emphasis on deep learning approaches combined with unbiased stereology. Major challenges include accurate and reproducible cell detection and segmentation of microscopic images from stained sections. Finally, we discuss potential improvements and future trends in deep learning applied to cell detection and segmentation.

A disector-based framework for the automatic optical fractionator.

Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep learning (DL) offers the possibility of improving throughput by automating the collection of stereology data. We have recently shown that DL can effectively achieve comparable accuracy to manual stereology but with higher repeatability, improved throughput, and less variation due to human factors by quantifying the total number of immunostained cells at their maximal profile of focus in extended depth of field (EDF) images. In the first of two novel contributions in this work, we propose a semi-automatic approach using a handcrafted Adaptive Segmentation Algorithm (ASA) to automatically generate ground truth on EDF images for training our deep learning (DL) models to automatically count cells using unbiased stereology methods. This update increases the amount of training data, thereby improving the accuracy and efficiency of automatic cell counting methods, without a requirement for extra expert time. The second contribution of this work is a Multi-channel Input and Multi-channel Output (MIMO) method using a U-Net deep learning architecture for automatic cell counting in a stack of z-axis images (also known as disector stacks). This DL-based digital automation of the ordinary optical fractionator ensures accurate counts through spatial separation of stained cells in the z-plane, thereby avoiding false negatives from overlapping cells in EDF images without the shortcomings of 3D and recurrent DL models. The contribution overcomes the issue of under-counting errors with EDF images due to overlapping cells in the z-plane (masking). We demonstrate the practical applications of these advances with automatic disector-based estimates of the total number of NeuN-immunostained neurons in a mouse neocortex. In summary, this work provides the first demonstration of automatic estimation of a total cell number in tissue sections using a combination of deep learning and the disector-based optical fractionator method.

Risk of Bleeding after Transcatheter Aortic Valve Replacement: impact of Preoperative Antithrombotic Regimens.

INTRODUCTION: Bleeding after transcatheter aortic valve replacement (TAVR) has a negative impact on the outcome of the procedure. Risk factors for bleeding vary widely in the literature, and the impact of preoperative antithrombotic agents has not been fully established. The objectives of our study were to assess bleeding after TAVR as defined by the Valve Academic Research Consortium-2 (VARC-2), identify its risk factors, and correlate with antithrombotic treatment in addition to its effect on procedural mortality. METHODS: The study included 374 patients who underwent TAVR from 2009 to 2018. We grouped the patients into four groups according to the VARC-2 definition of bleeding. Group 1 included patients without bleeding (n=265), group 2 with minor bleeding (n=22), group 3 with major bleeding (n=61), and group 4 with life-threatening bleeding (n=26). The median age was 78 (25th-75th percentiles: 71-82), and 226 (60.4%) were male. The median EuroSCORE was 3.4 (2-6.3), and there was no difference among groups (P=0.886). The TAVR approach was transfemoral (90.9%), transapical (5.6%), and trans-subclavian (1.9%). Results: Predictors of bleeding were stroke (OR: 2.465; P=0.024) and kidney failure (OR: 2.060; P=0.046). Preoperative single and dual antiplatelet therapy did not increase the risk of bleeding (P=0.163 and 0.1, respectively). Thirty-day mortality occurred in 14 patients (3.7%), and was significantly higher in patients with life-threatening bleeding (n=8 [30.8%]; P<0.001). Conclusion: Bleeding after TAVR is common and can be predicted based on preprocedural comorbidities. Preprocedural antithrombotic therapy did not affect bleeding after TAVR in our population.

Outcomes After Transcatheter Aortic Valve Replacement in Patients with Severe Aortic Stenosis and Diastolic Dysfunction.

OBJECTIVES: Left ventricular diastolic dysfunction (LVDD) in patients undergoing transcatheter aortic valve replacement (TAVR) is associated with poor outcomes; however, the effect of its severity is controversial. We sought to assess the impact of diastolic dysfunction on hospital outcomes and survival after TAVR and identify prognostic factors. METHODS: We included patients who underwent TAVR for severe aortic stenosis with preexisting LVDD from 2009 to 2018 (n = 325). Patients with prior mitral valve surgery (n = 4), atrial fibrillation (n = 39), missing or poor baseline diastolic dysfunction assessment (n = 36) were excluded. The primary endpoint was all-cause mortality. 246 patients were included in the study. RESULTS: The median age was 80 years (25th and 75th percentiles:75-86.7), 154 (62.6%) were males and the median EuroSCORE II was 4.3 (2.2-8). Patients with severe LVDD had significantly higher EuroSCORE, and lower ejection fraction (p < 0.001). There was no difference in post-TAVR new atrial fibrillation (p = 0.912), pacemaker insertion (p = 0.528), stroke (p = 0.76), or hospital mortality (p = 0.95). Patients with severe LVDD had longer hospital stay (p = 0.036). The grade of LVDD did not affect survival (log-rank = 0.145) nor major adverse cardiovascular events (log-rank = 0.97). Predictors of mortality were; low BMI (HR: 0.95 (0.91-0.99); p = 0.019), low sodium (0.93 (0.82-2.5); p = 0.021), previous PCI (HR: 1.6 (1.022-2.66); p = 0.04), E-peak (HR: 1.01 (1.002-1.019); p = 0.014) and implantation of more than one device (HR: 3.55 (1.22-10.31); p = 0.02). CONCLUSION: Transcatheter aortic valve replacement is feasible in patients with diastolic dysfunction, and the degree of diastolic dysfunction did not negatively affect the outcome. Long-term outcomes in those patients were affected by the preoperative clinical state and procedure-related factors.

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise and more efficient automatic approaches for quantifying morphometric changes in biological tissue, we have shown that both deep learning-based and hand-crafted algorithms can estimate the total number of histologically stained cells at their maximal profile of focus in Extended Depth of Field (EDF) images. Deep learning-based approaches show accuracy comparable to manual counts on EDF images but significant enhancement in reproducibility, throughput efficiency and reduced error from human factors. However, a majority of the automated counts are designed for single-immunostained tissue sections. NEW METHOD: To expand the automatic counting methods to more complex dual-staining protocols, we developed an adaptive method to separate stain color channels on images from tissue sections stained by a primary immunostain with secondary counterstain. COMPARISON WITH EXISTING METHODS: The proposed method overcomes the limitations of the state-of-the-art stain-separation methods, like the requirement of pure stain color basis as a prerequisite or stain color basis learning on each image. RESULTS: Experimental results are presented for automatic counts using deep learning-based and hand-crafted algorithms for sections immunostained for neurons (Neu-N) or microglial cells (Iba-1) with cresyl violet counterstain. CONCLUSION: Our findings show more accurate counts by deep learning methods compared to the handcrafted method. Thus, stain-separated images can function as input for automatic deep learning-based quantification methods designed for single-stained tissue sections.

Cavity-Enhanced Raman and Helmholtz Resonator Photoacoustic Spectroscopy to Monitor the Mixed Sugar Metabolism of E. coli.

We introduce and compare two powerful new techniques for headspace gas analysis above bacterial batch cultures by spectroscopy, Raman spectroscopy enhanced in an optical cavity (CERS), and photoacoustic detection in a differential Helmholtz resonator (DHR). Both techniques are able to monitor O2 and CO2 and its isotopomers with excellent sensitivity and time resolution to characterize bacterial growth and metabolism. We discuss and show some of the shortcomings of more conventional optical density (OD) measurements if used on their own without more sophisticated complementary measurements. The spectroscopic measurements can clearly and unambiguously distinguish the main phases of bacterial growth in the two media studied, LB and M9. We demonstrate how 13C isotopic labeling of sugars combined with spectroscopic detection allows the study of bacterial mixed sugar metabolism to establish whether sugars are sequentially or simultaneously metabolized. For E. coli, we have characterized the shift from glucose to lactose metabolism without a classic diauxic lag phase. DHR and CERS are shown to be cost-effective and highly selective analytical tools in the biosciences and in biotechnology, complementing and superseding existing conventional techniques. They also provide new capabilities for mechanistic investigations and show a great deal of promise for use in stable isotope bioassays.

Diode laser photoacoustic spectroscopy of CO2, H2S and O2 in a differential Helmholtz resonator for trace gas analysis in the biosciences and petrochemistry.

Photoacoustic spectroscopy in a differential Helmholtz resonator has been employed with near-IR and red diode lasers for the detection of CO2, H2S and O2 in 1 bar of air/N2 and natural gas, in static and flow cell measurements. With the red distributed feedback (DFB) diode laser, O2 can be detected at 764.3 nm with a noise equivalent detection limit of 0.60 mbar (600 ppmv) in 1 bar of air (35-mW laser, 1-s integration), corresponding to a normalised absorption coefficient α = 2.2 × 10-8 cm-1 W s1/2. Within the tuning range of the near-IR DFB diode laser (6357-6378 cm-1), CO2 and H2S absorption features can be accessed, with a noise equivalent detection limit of 0.160 mbar (160 ppmv) CO2 in 1 bar N2 (30-mW laser, 1-s integration), corresponding to a normalised absorption coefficient α = 8.3 × 10-9 cm-1 W s1/2. Due to stronger absorptions, the noise equivalent detection limit of H2S in 1 bar N2 is 0.022 mbar (22 ppmv) at 1-s integration time. Similar detection limits apply to trace impurities in 1 bar natural gas. Detection limits scale linearly with laser power and with the square root of integration time. At 16-s total measurement time to obtain a spectrum, a noise equivalent detection limit of 40 ppmv CO2 is obtained after a spectral line fitting procedure, for example. Possible interferences due to weak water and methane absorptions have been discussed and shown to be either negligible or easy to correct. The setup has been used for simultaneous in situ monitoring of O2, CO2 and H2S in the cysteine metabolism of microbes (E. coli), and for the analysis of CO2 and H2S impurities in natural gas. Due to the inherent signal amplification and noise cancellation, photoacoustic spectroscopy in a differential Helmholtz resonator has a great potential for trace gas analysis, with possible applications including safety monitoring of toxic gases and applications in the biosciences and for natural gas analysis in petrochemistry. Graphical abstract.

Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology.

In recent decades stereology-based studies have played a significant role in understanding brain aging and developing novel drug discovery strategies for treatment of neurological disease and mental illness. A major obstacle to further progress in a wide range of neuroscience sub-disciplines remains the lack of high-throughput technology for stereology analyses. Though founded on methodologically unbiased principles, commercially available stereology systems still rely on well-trained humans to manually count hundreds of cells within each region of interest (ROI). Even for a simple study with 10 controls and 10 treated animals, cell counts typically require over a month of tedious labor and high costs. Furthermore, these studies are prone to errors and poor reproducibility due to human factors such as subjectivity, variable training, recognition bias, and fatigue. Here we propose a deep neural network-stereology combination to automatically segment and estimate the total number of immunostained neurons on tissue sections. Our three-step approach consists of (1) creating extended-depth-of-field (EDF) images from z-stacks of images (disector stacks); (2) applying an adaptive segmentation algorithm (ASA) to label stained cells in the EDF images (i.e., create masks) for training a convolutional neural network (CNN); and (3) use the trained CNN model to automatically segment and count the total number of cells in test disector stacks using the optical fractionator method. The automated stereology approach shows less than 2% error and over 5× greater efficiency compared to counts by a trained human, without the subjectivity, tedium, and poor precision associated with conventional stereology.

Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening.

Low-dose computed tomography (LDCT) plays a critical role in the early detection of lung cancer. Despite the life-saving benefit of early detection by LDCT, there are many limitations of this imaging modality including high rates of detection of indeterminate pulmonary nodules. Radiomics is the process of extracting and analyzing image-based, quantitative features from a region-of-interest which then can be analyzed to develop decision support tools that can improve lung cancer screening. Although prior published research has shown that delta radiomics (i.e., changes in features over time) have utility in predicting treatment response, limited work has been conducted using delta radiomics in lung cancer screening. As such, we conducted analyses to assess the performance of incorporating delta with conventional (non delta) features using machine learning to predict lung nodule malignancy. We found the best improved area under the receiver operating characteristic curve (AUC) was 0.822 when delta features were combined with conventional features versus an AUC 0.773 for conventional features only. Overall, this study demonstrated the important utility of combining delta radiomics features with conventional radiomics features to improve performance of models in the lung cancer screening setting.

Severe aortic regurgitation complicating Takayasu's arteritis.

We present an uncommon case of a 48-year-old female patient with symptomatic presentation of a severe aortic regurgitation with aneurysm of the ascending aorta and progressive dyspnea. Detailed investigation of laboratory tests and imaging identified Takayasu's arteritis (TA) as the underlying etiology. Computed tomography scan revealed complete occlusion of the right carotid artery as well as stenosis at the origins of left subclavian and vertebral arteries. In addition, cardiac magnetic resonance angiogram showed aneurysm at the proximal segment of right subclavian artery. Intervention with corticosteroids effectively diminished the need for immediate surgical intervention. Treating physicians should always consider differential diagnosis of TA in the presence of atypical clinical findings in all patients with cardiac problems especially when there is valve involvement.