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Pathology Pathology

The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues.

In The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society

Advances in reagents, methodologies, analytic platforms, and tools have resulted in a dramatic transformation of the research pathology laboratory. These advances have increased our ability to efficiently generate substantial volumes of data on the expression and accumulation of mRNA, proteins, carbohydrates, signaling pathways, cells, and structures in healthy and diseased tissues that are objective, quantitative, reproducible, and suitable for statistical analysis. The goal of this review is to identify and present how to acquire the critical information required to measure changes in tissues. Included is a brief overview of two morphometric techniques, image analysis and stereology, and the use of artificial intelligence to classify cells and identify hidden patterns and relationships in digital images. In addition, we explore the importance of preanalytical factors in generating high-quality data. This review focuses on techniques we have used to measure proteoglycans, glycosaminoglycans, and immune cells in tissues using immunohistochemistry and in situ hybridization to demonstrate the various morphometric techniques. When performed correctly, quantitative digital pathology is a powerful tool that provides unbiased quantitative data that are difficult to obtain with other methods.

Davis A Sally, Chang Mary Y, Brune Jourdan E, Hallstrand Teal S, Johnson Brian, Lindhartsen Sarah, Hewitt Stephen M, Frevert Charles W


artificial intelligence, asthma, digital pathology, extracellular matrix, glycosaminoglycans, image analysis, immunohistochemistry, in situ hybridization, influenza, machine learning, proteoglycans, stereology

Radiology Radiology

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully-Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1,204 Heathy Adults Using Unenhanced CT as Reference Standard.

In AJR. American journal of roentgenology

Background: Hepatic attenuation at unenhanced CT is linearly correlated with MR proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. Objective: To evaluate liver steatosis categorization on contrast-enhanced CT using a fully-automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. Materials and Methods: A fully-automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis), 10%, and 15% (moderate); PDFF<5% was considered normal. Results: Using unenhanced CT as reference, estimated PDFF was ≥5% (mild steatosis), ≥10%, and ≥15% (moderate) in 50.1% (n=603), 12.5% (n=151) and 4.8% (n=58) of patients, respectively. ROC-AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation <90 HU had steatosis (PDFF≥5%); this <90 HU threshold achieved sensitivity 75.9% and specificity 95.7% for moderate steatosis (PDFF≥15%). Liver attenuation <100 HU achieved sensitivity 34.0% and specificity 94.2% for any steatosis (PDFF≥5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference <-10 HU had moderate steatosis (PDFF≥15%); a liver-spleen difference <5 HU achieved sensitivity 91.4% and specificity 95.0% for moderate steatosis. Liver-spleen difference <10 HU achieved sensitivity 29.5% and specificity 95.5% for any steatosis (PDFF≥5%). Conclusion: Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully-automated deep-learning CT tool may allow objective categorical assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. Clinical Impact: If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Pickhardt Perry J, Blake Glen M, Graffy Peter M, Sandfort Veit, Elton Daniel C, Perez Alberto A, Summers Ronald M


General General

Discovery, Design, and Structural Characterization of Alkane Producing Enzymes across the Ferritin-like Superfamily.

In Biochemistry

To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADO), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts we discovered latent ADO-like function across the Ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of E. coli Class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.

Mak Wai Shun, Wang Xiaokang, Arenas Rigoberto, Cui Youtian, Bertolani Steve J, Deng Wen Qiao, Tagkopoulos Ilias, Wilson David K, Siegel Justin B


Surgery Surgery

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

In Lab on a chip

The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical power and sensitivity that are unattainable with current technologies. To close this gap, we present a large-scale, integrative imaging flow cytometry platform and strategy that allows hierarchical analysis of intrinsic morphological descriptors of single-cell optical and mass density within a population of millions of cells. The optofluidic cytometry system also enables the synchronous single-cell acquisition of and correlation with fluorescently labeled biochemical markers. Combined with deep neural network and transfer learning, this massive single-cell profiling strategy demonstrates the label-free power to delineate the biophysical signatures of the cancer subtypes, to detect rare populations of cells in the heterogeneous samples (10-5), and to assess the efficacy of targeted therapeutics. This technique could spearhead the development of optofluidic imaging cell-based assays that stratify the underlying physiological and pathological processes based on the information-rich biophysical cellular phenotypes.

Siu Dickson M D, Lee Kelvin C M, Lo Michelle C K, Stassen Shobana V, Wang Maolin, Zhang Iris Z Q, So Hayden K H, Chan Godfrey C F, Cheah Kathryn S E, Wong Kenneth K Y, Hsin Michael K Y, Ho James C M, Tsia Kevin K


General General

Developing Employment Environments Where Individuals with ASD Thrive: Using Machine Learning to Explore Employer Policies and Practices.

In Brain sciences

An online survey instrument was developed to assess employers' perspectives on hiring job candidates with Autism Spectrum Disorder (ASD). The investigators used K-means clustering to categorize companies in clusters based on their hiring practices related to individuals with ASD. This methodology allowed the investigators to assess and compare the various factors of businesses that successfully hire employees with ASD versus those that do not. The cluster analysis indicated that company structures, policies and practices, and perceptions, as well as the needs of employers and employees, were important in determining who would successfully hire individuals with ASD. Key areas that require focused policies and practices include recruitment and hiring, training, accessibility and accommodations, and retention and advancement.

Griffiths Amy Jane, Hanson Amy Hurley, Giannantonio Cristina M, Mathur Sneha Kohli, Hyde Kayleigh, Linstead Erik


autism spectrum disorder, employment, machine learning

General General

Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit during COVID-19: An Observational Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic is exerting a devastating impact on mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit.

OBJECTIVE : We leverage natural language processing (NLP) with the goal of characterizing changes in fifteen of the world's largest mental health support groups (e.g., r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with eleven non-mental health groups (e.g., r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic.

METHODS : We create and release the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyze trends from 90 text-derived features such as sentiment analysis, personal pronouns, and a "guns" semantic category. Using supervised machine learning, we classify posts into their respective support group and interpret important features to understand how different problems manifest in language. We apply unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic.

RESULTS : We find that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately two months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories "economic stress", "isolation", and "home" while others such as "motion" significantly decreased. We find that support groups related to attention deficit hyperactivity disorder (ADHD), eating disorders (ED), and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discover that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ = -0.96, P<.001). Using unsupervised clustering, we find the Suicidality and Loneliness clusters more than doubled in amount of posts during the pandemic. Specifically, the support groups for borderline personality disorder and post-traumatic stress disorder became significantly associated with the Suicidality cluster. Furthermore, clusters surrounding Self-Harm and Entertainment emerged.

CONCLUSIONS : By using a broad set of NLP techniques and analyzing a baseline of pre-pandemic posts, we uncover patterns of how specific mental health problems manifest in language, identify at-risk users, and reveal the distribution of concerns across Reddit which could help provide better resources to its millions of users. We then demonstrate that textual analysis is sensitive to uncover mental health complaints as they arise in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests from the present or the past.


Low Daniel M, Rumker Laurie, Talker Tanya, Torous John, Cecchi Guillermo, Ghosh Satrajit S