bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards personalised management for non-variceal upper gastrointestinal bleeding
Authors: Sung Joseph J Y , Laine Loren , Kuipers Ernst J , Barkun Alan N
Published: March 2021

Guidelines from national and international professional societies on upper gastrointestinal bleeding highlight the important clinical issues but do not always identify specific management strategies pertaining to individual patients. Optimal treatment should consider the personal needs of an individual patient and the pertinent resources and experience available at the point of care. This article integrates international guidelines and consensus into three stages of management: pre-endoscopic assessment and treatment, endoscopic evaluation and haemostasis and postendoscopic management. We emphasise the need for personalised management strategies based on patient characteristics, nature of bleeding lesions and the clinical setting including available resources.
Core needle biopsy wash as a tool for acquiring additional diagnostic material for laboratory testing
Authors: Mojica Wilfrido , Cwiklinski Katherine , Jin Xiaobing , Liu Weiguo , Yergeau Donald
Published: March 2021

Aims

To determine if a simple prewash step added to the processing workflow of tissue procurement by a core needle biopsy device will recover enough cells to expand the laboratory testing armamentarium.

Methods

Tissue was obtained from unfixed resection specimens using a core needle device and washed in a buffered solution before fixation. This creates a liquid aliquot from which dislodged cells can be kept and separated from the tissue specimen, the latter of which can then undergo traditional formalin-fixed, paraffin-embedded processing.

Results

Cells dislodged from the tissue during the biopsy procedure are recoverable, are representative of the tissue section and of sufficient quantities for additional laboratory testing.

Conclusions

The core needle biopsy wash is an under-recognised and underutilised approach to extending the diagnostic capabilities of the limited amount of targeted material obtained during this common procedure. The ability to recover supplemental amounts of diagnostic material yields great potential as a substrate for a multitude of current and developing laboratory assays.
Automated text messaging follow-up for patients who receive peripheral nerve blocks
Authors: Gessner Daniel , Hunter Oluwatobi O , Kou Alex , Mariano Edward R
Published: March 2021

Background

Routine follow-up of patients who receive a nerve block for ambulatory surgery typically consists of a phone call from a regional anesthesia clinician. This process can be burdensome for both patients and clinicians but is necessary to assess the efficacy and complication rate of nerve blocks.

Methods

We present our experience developing an automated system for completing follow-up via short message service text messaging and our preliminary results using it at three clinical sites. The system is built on REDCap, a secure online research data capture platform developed by Vanderbilt University and currently available worldwide.

Results

Our automated system queried patients who received a variety of nerve block techniques, assessed patient-reported nerve block duration, and surveyed patients for potential complications. Patient response rate to text messaging averaged 91% (higher than our rates of daily phone contact reported previously) for patients aged 18 to 90 years.

Conclusions

Given the wide availability of REDCap, we believe this automated text messaging system can be implemented in a variety of health systems at low cost with minimal technical expertise and will improve both the consistency of patient follow-up and the service efficiency of regional anesthesia practices.
A risk assessment tool for tailings storage facilities
Authors: Chovan Karen M. , Julien Michel R. , Ingabire Edouardine-Pascale , James Michael , Masengo Édouard , Lépine Thomas , Lavoie Pascal
Published: March 2021

The recent occurrence of several major failures of tailings storage facilities (TSF) has caused the mining industry to focus on significantly improving the engineering and management (design, construction, operation and monitoring) of these structures to reduce their environmental impact. This effort is led by the Mining Association of Canada, which mandates the application of risk assessment in tailings management. Due to the very complex nature of TSF, such as phased design and construction, continuous operation, and evolving guidelines and practices over many years, the application of traditional risk assessment tools has limitations. A risk assessment tool specifically developed for TSF management is presented. This tool is based on the work of Silva et al. from 2008 that relates the annual probability of failure to the factor of safety and the level of engineering. This relationship was modified to reflect current practice. The annual probability of failure was then combined with a consequence rating to produce a rational and quantifiable evaluation of risk. The risk assessment tool provides detailed information on the level of practice of a structure, the corresponding annual probability of failure as well as the associated risk. Validation of the tool included application to a recent well-documented failure.
Isoliquiritigenin Suppresses EMT-Induced Metastasis in Triple-Negative Breast Cancer through miR-200c/C-JUN/β-Catenin
Authors: Peng Fu , Tang Hailin , Du Junrong , Chen Jianping , Peng Cheng
Published: March 2021

Triple-negative breast cancer (TNBC) is the subtype of breast cancer with more aggressive growth and metastasis and without efficient therapies. Hence, it is worthwhile to search for potential effective drug candidates. According to our previous study, isoliquiritigenin (ISL) exerted a potent anticancer effect on breast cancer proliferation. Its effect on TNBC growth, metastasis and mechanism deserves further investigation. In this study, PCR array screened a significant increase of miR-200c in BT-549 and MDA-MB-231 cells after ISL treatment, and ISH exerted that miR-200c was expressed at a low level in breast cancer tissue of patients. We also found that ISL could up-regulate miR-200c, resulting in the inhibition of epithelial-mesenchymal transition. Meanwhile, ISL could inhibit metastasis and tumor growth in nude mice models through the increase of miR-200c. Further study displayed that ISL decreased c-Jun expression through the increase of miR-200c. Interestingly, we also detected that ISL might increase miR-200c expression through the demethylation of miR-200c promoter region. These findings indicated that ISL could be potentially developed as a novel drug candidate for TNBC in microRNA-based cancer therapies.
20(S)-Ginsenoside Rg3 Inhibits Lung Cancer Cell Proliferation by Targeting EGFR-Mediated Ras/Raf/MEK/ERK Pathway
Authors: Liang Yuan , Zhang Tiehua , Jing Siyuan , Zuo Peng , Li Tiezhu , Wang Yongjun , Xing Shaochen , Zhang Jie , Wei Zhengyi
Published: March 2021

Lung cancer is the leading cause of cancer death in the world and classified into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). As tyrosine kinase inhibitors (TKIs), several triterpenoid saponins can target to epidermal growth factor receptor (EGFR), a widely used molecular therapeutic target, to exhibit remarkable anti-proliferative activities in cancer cells. As one of triterpenoid saponins, 20([Formula: see text])-ginsenoside Rg3 [20([Formula: see text])-Rg3] was confirmed to be an EGFR-TKI in this work. According to the quantitative real-time reverse transcription-PCR (qRT-PCR) and immunoblotting analysis, 20([Formula: see text])-Rg3 was certified to play a key role on EGFR/Ras/Raf/MEK/ERK signal pathway regulation. Our data demonstrated that 20([Formula: see text])-Rg3 might block the cell cycle at the G0/G1 phase by downregulating CDK2, Cyclin A2, and Cyclin E1. Molecular docking suggested that the combination of both hydrophobic and hydrogen-bonding interactions may help stabilizing the 20([Formula: see text])-Rg3-EGFR binding. Furthermore, their binding stability was assessed by molecular dynamics simulation. Taken together, these data provide the evidence that 20([Formula: see text])-Rg3 could prohibit A549 cell proliferation, probably by arresting the cell cycle at the G0/G1 phase via the EGFR/Ras/Raf/MEK/ERK pathway.
Span of the Jones polynomials of certain v-adequate virtual links
Authors: Okamura Minori , Sakai Keiichi
Published: March 2021

It is known that the Kauffman–Murasugi–Thislethwaite type inequality becomes an equality for any (possibly virtual) adequate link diagram. We refine this condition. As an application we obtain a criterion for virtual link diagram with exactly one virtual crossing to represent a properly virtual link.
Tied links in the solid torus
Authors: Flores Marcelo
Published: March 2021

We introduce the concept of tied links in the solid torus, which generalizes naturally the concept of tied links in [Formula: see text] previously introduced by Aicardi and Juyumaya. We also define an invariant of these tied links by using skein relations, and we then recover this invariant by using Jones’ method over the bt-algebra of type [Formula: see text] and the Markov trace defined on this.
A FRACTAL MODEL FOR PREDICTING THE EFFECTIVE THERMAL CONDUCTIVITY OF ROUGHENED POROUS MEDIA WITH MICROSCALE EFFECT
Authors: XIAO BOQI , ZHANG MIN , CHEN HANXIN , CAO JIYIN , LONG GONGBO , ZHAO ZHENG
Published: March 2021

The effective thermal conductivity (ETC) of roughened porous media (RPM) is of interest in a number of applications of heat transfer. In this work, a fractal analytical model for the ETC of RPM with microscale effect is proposed. The proposed fractal model is expressed in terms of relative roughness, the molecular mean free path, porosity, fractal dimensions (pore area fractal dimension and tortuosity fractal dimension), maximum pore diameter, capillary straight length, and the thermal conductivity of the solid matrix and gas. It is observed that the dimensionless ETC of RPM decreases with increasing relative roughness, pore area fractal dimension and tortuosity fractal dimension. Besides, it is found that the dimensionless ETC of RPM increases with thermal conductivity ratio of gas phase over solid phase. In addition, it is found that the dimensionless ETC of RPM is slightly dependent on the relative roughness and tortuosity fractal dimension when [Formula: see text]. The determined dimensionless ETC of RPM is in good agreement with experimental data and existing models reported in the literature. With the proposed fractal model, the physical mechanisms of heat transport through RPM with microscale effect are better elucidated. Every parameter in the fractal analytical model has clear physical meaning, with no empirical constant.
A Novel Hybrid Filter for Image Despeckling Based On Improved Adaptive Wiener Filter, Bilateral Filter and Wavelet Filter
Authors: Salehi Hadi , Vahidi Javad
Published: March 2021

Images are widely used in engineering. But, some images such as medical ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, we suggest three phases and three denoising filters. In the first phase, the coefficient of variation is computed from the noisy image. Next, fuzzy c-means (FCM) is applied to the coefficients of variation. Applying FCM leads to the fuzzy classification of image regions. Next, the second phase is a hybrid of the three denoising filters. Fast bilateral filter (BF) for homogeneous regions, improved the adaptive wiener filters (AWFs) and wavelet filter that are applied on homogeneous, detail and edge regions, respectively. The proposed improved AWF has been developed from the AWF. In the third phase, the output image is evaluated by the fuzzy logic approach. Thus, with three phases, the proposed method has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.
A dual regulatory effect of naringenin on bone homeostasis in two diabetic mice models
Authors: Liu Shufen , Dong Jingcheng , Bian Qin
Published: March 2021

Objective: Naringenin (NAR), a flavanone in citrus fruits, has been reported to have both anti-diabetic and anti-osteoporotic effects. This study aimed to explore the effect of NAR on bone homeostasis under diabetic condition.

Methods: High fat diet and streptozotocin (STZ) induced type 1 diabetic (T1DM) and leptin receptor knockout (db/db) type 2 diabetic (T2DM) mice were used to evaluate NAR effects. Melbine (DMBG) was administrated as positive control. Body weight and fasting blood glucose were monitored weekly and monthly. After 8 weeks and 74 days treatment, bone mass was evaluated by microcomputed tomography ([Formula: see text]CT) including BV/TV, Tb.N, and Tb.Th, as well as histological and histomorphometric detection. Bone resorption rate indicated by C-terminal telopeptide of type I collagen (CTIX) and N-terminal propeptide of type I procollagen (PINP) was examined by ELISA assays.

Results: NAR treatment reduced body weight and blood glucose in both diabetic models, and had better hypoglycemic effect than DMBG at early stage. High fat diet and STZ-treated mice lost while db/db mice gained bone mass. NAR improved bone microarchitecture by regulating the related parameters to the similar levels as the control. Osteoblast activity was little affected, but osteoclast function was decreased in NAR-treated STZ mice. Consistently, NAR reduced bone resorption rate which was increased in both diabetic models.

Conclusion: NAR exerts an anti-diabetic effect by lowering elevated level of blood glucose, regulating impaired bone mass, and reducing overactivated bone resorption rate in T1DM and T2DM conditions. Naringenin has a potential to prevent diabetes induced impairment in bone.
Immune System Investigation Using Parasitic Helminths
Authors: Douglas Bonnie , Oyesola Oyebola , Cooper Martha M. , Posey Avery , Tait Wojno Elia , Giacomin Paul R. , Herbert De'Broski R.
Published: March 2021

Coevolutionary adaptation between humans and helminths has developed a finely tuned balance between host immunity and chronic parasitism due to immunoregulation. Given that these reciprocal forces drive selection, experimental models of helminth infection are ideally suited for discovering how host protective immune responses adapt to the unique tissue niches inhabited by these large metazoan parasites. This review highlights the key discoveries in the immunology of helminth infection made over the last decade, from innate lymphoid cells to the emerging importance of neuroimmune connections. A particular emphasis is placed on the emerging areas within helminth immunology where the most growth is possible, including the advent of genetic manipulation of parasites to study immunology and the use of engineered T cells for therapeutic options. Lastly,we cover the status of human challenge trials with helminths as treatment for autoimmune disease, which taken together, stand to keep the study of parasitic worms at the forefront of immunology for years to come.
Production and Function of Immunoglobulin A
Authors: Hand Timothy W. , Reboldi Andrea
Published: March 2021

Among antibodies, IgA is unique because it has evolved to be secreted onto mucosal surfaces. The structure of IgA and the associated secretory component allow IgA to survive the highly proteolytic environment of mucosal surfaces but also substantially limit IgA's ability to activate effector functions on immune cells. Despite these characteristics, IgA is critical for both preventing enteric infections and shaping the local microbiome. IgA's function is determined by a distinct antigen-binding repertoire, composed of antibodies with a variety of specificities, from permissive polyspecificity to cross-reactivity to exquisite specificity to a single epitope, which act together to regulate intestinal bacteria. Development of the unique function and specificities of IgA is shaped by local cues provided by the gut-associated lymphoid tissue, driven by the constantly changing environment of the intestine and microbiota.
MWCNTs/PEDOT: PSS Composite as Guiding Layer on Screen-Printed Carbon Electrode for Linear Range Lactate Detection
Authors: Ali Muhsin , Khalid Muhammad Asad Ullah , Kim Young Su , Soomro Afaque Manzoor , Hussain Saddam , Doh Yang Hoi , Choi Kyung Hyung
Published: March 2021

The emerging field of nanomaterials could be utilized in biosensors for addressing challenging applications due to its abundant strategic properties. Herein, a composite of multi-walled carbon nanotubes (MWCNTs) and poly (3,4-ethylene dioxythiophene)-poly(styrene sulfonate) (PEDOT: PSS) was synthesized, and used as guiding layer on screen printed carbon electrode (SPCE) for lactate detection. Lactate plays a substantial role in health care applications. Fundamental characteristics of the composite modified SPCE were studied using FE-SEM, EDS, and Raman spectroscopy and sensor performance was analyzed by electrochemical methods. AFM was used to thoroughly study the screen-printed carbon electrode and as-deposited MWCNT/PEDOT:PSS composite film topography. Various parameters were optimized to achieve the best performance of lactate sensor. The developed sensor provided a wide linear range response (R2 = 0.97) from 1 mM to 10 mM for buffer samples with 35.224 μA mM−1 sensitivity. The proposed sensor was applied for to detect lactate in cancer (MCF-7) cells media. A calculated LOD 4.0 ± 5 μM (S/N) was achieved and the results showed a linear response up to 7 mM. As a result, the advanced approach could be applied for the detection of a range of metabolites using respective enzymes. This approach could open on-line lactate detection in organ-on-a-chip applications.
rGO/PEDOT:PSS/NiMn2O4 Nanohybrid: An Inexpensive Anode Catalyst for Methanol and Ethylene Glycol Electro-Oxidation
Authors: Baruah Bhagyalakhi , Kumar Ashok
Published: March 2021

Present work reports the development of a mesoporous ternary nanohybrid (GPN) based on NiMn2O4, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and reduced graphene oxide (rGO) for methanol (MeOH) and ethylene glycol (EG) oxidation. Initially, NiMn2O4 nanoparticles have been synthesized using solvothermal-assisted calcination and then polymerization of EDOT monomer has been done in presence of rGO nanosheets and NiMn2O4 nanoparticles to form GPN nanohybrid. The current densities of 70.4 mA cm−2 at an onset potential of 0.21 V in 0.5 M MeOH and 53 mA cm−2 at an onset potential of 0.32 V in 0.5 M EG solution have been recorded at GPN modified indium tin oxide (ITO) electrode. This electrode exhibits a current retention of 87% in 0.5 M MeOH and 81% in 0.5 M EG solution after 700 cycles. The improved electrocatalytic activity and cyclic stability of GPN can be attributed to the presence of rich binary active sites of Ni and Mn species, large surface area and high electrical conductivity of rGO and uniform dispersion of NiMn2O4 nanoparticles over PEDOT:PSS. This work demonstrates the potential of GPN nanohybrid as an alternative to platinum towards MeOH and EG electro-oxidation.
Metformin ameliorates bladder dysfunction in a rat model of partial bladder outlet obstruction
Authors: Chen Lipeng , Lv Linchen , Zhang Lekai , Gao Zhengdong , Liu Yaxiao , Wang Shuai , Zhou Nan , Xia Yangyang , Cui Jianfeng , Jiang Xuewen , Zhang Xiulin , Li Yan , Shi Benkang
Published: March 2021

The present study in a rat model showed that oral administration of metformin alleviated inflammation following partial bladder outlet obstruction in the early phase and ameliorated bladder fibrosis as well as bladder dysfunction by long-term treatment. Our study indicated that metformin is a potential drug to inhibit bladder remodeling and alleviate bladder dysfunction. Clinical trials are needed to validate the effect of metformin on the bladder dysfunction and bladder fibrosis in the future.
Nonexistence of Global Solutions for Coupled System of Pseudoparabolic Equations with Variable Exponents and Weak Memories
Authors: Zennir Kh. , Dridi H. , Alodhaibi S. , Alkhalaf S.
Published: March 2021

The most important behavior for evolution system is the blow-up phenomena because of its wide applications in modern science. The article discusses the finite time blowup that arise under an appropriate conditions. The nonsolvability of boundary value problem for damped pseudoparabolic differential equations with variable exponents is investigated. Such problem has been previously studied in the case if

p

and

q

are constants. New here is the case of variables of nonlinearity

p

and

q

which make the problem has a scientific interest.
Hammerstein Box-Jenkins System Identification of the Cascaded Tanks Benchmark System
Authors: Aljamaan Ibrahim A. , Al-Dhaifallah Mujahed M. , Westwick David T.
Published: March 2021

A common process control application is the cascaded two-tank system, where the level is controlled in the second tank. A nonlinear system identification approach is presented in this work to predict the model structure parameters that minimize the difference between the estimated and measured data, using benchmark datasets. The general suggested structure consists of a static nonlinearity in cascade with a linear dynamic filter in addition to colored noise element. A one-step ahead prediction error-based technique is proposed to estimate the model. The model is identified using a separable least squares optimization, where only the parameters that appear nonlinearly in the output of the predictor are solved using a modified Levenberg–Marquardt iterative optimization approach, while the rest are fitted using simple least squares after each iteration. Finally, MATLAB simulation examples using benchmark data are included.
Determination of First Arrival Wave Type of Microseismic Signals and Approach to Wave Velocity Correction
Authors: Song GuangDong , Cheng JiuLong , Hu BinXin , Zhu Feng , Zhang Hua , Liu TongYu , Sun Tong , Grattan Kenneth T. V.
Published: March 2021

Given the complex environment experienced in working mines, the vibration waves produced by processes such as rock fracture in deep formations usually show interference effects when monitored due to other signals, the so‐called “clutter” in the signal, which are interfered with the clutter. At the same time, owing to the influence of system noise, the first arrival time and the arrival time difference values of the signals obtained cannot easily be determined accurately. The propagation model for the microseismic signals experienced and the discrimination method used to determine the first arrival wave type can be established using knowledge of the spatial geometry between the sensors used and the seismic source. Thus, the filtering of the actual from the abnormal wave signals is possible. Using the theory of signal cross‐correlation in this work, a correction method for the arrival velocity of the first microseismic signal has been proposed and evaluated. By calculating the cross‐correlation coefficient of the same source vibration signal and finding the position that corresponds to the maximum value of the cross‐correlation coefficient, the arrival time difference between the signals seen in the two channels is obtained. Thus, the key conclusions can be drawn from the experiments carried out: when the signal‐to‐noise ratio of the original signal is low, the time difference can still be determined with high accuracy. Further, a wave velocity correction criterion has also been proposed, where the velocity correction of the S wave or the R wave can be realized by combining the spatial coordinate information on the blasting point and an algorithm representing the signal cross‐correlation to arrival time difference is used.
Fractals via Generalized Jungck–S Iterative Scheme
Authors: Chen Zhihua , Tanveer Muhammad , Nazeer Waqas , Wu Jing
Published: March 2021

The purpose of this research is to introduce a Jungck–S iterative method with

m
,

h

1

,

h

2

–convexity and hence unify different comparable iterative schemes existing in the literature. A Jungck–S orbit is constructed, and escape radius is derived with our scheme. A new escape radius is also obtained for generating the fractals. Julia and Mandelbrot set are visualized with the help of proposed algorithms based on our iterative scheme. Moreover, we present some complex graphs of Julia and Mandelbrot sets using the derived orbit and discuss their nature in detail.
Systematic Analysis of Intronic miRNAs Reveals Cooperativity within the Multicomponent FTX Locus to Promote Colon Cancer Development
Authors: Kwok Zhi Hao , Zhang Bin , Chew Xiao Hong , Chan Jia Jia , Teh Velda , Yang Henry , Kappei Dennis , Tay Yvonne
Published: March 2021

Approximately half of all miRNA reside within intronic regions and are often cotranscribed with their host genes. However, most studies of intronic miRNA focus on individual miRNA, while conversely most studies of protein-coding and noncoding genes frequently ignore any intron-derived miRNA. We hypothesize that the individual components of such multigenic loci may play cooperative or competing roles in driving disease progression and that examining the combinatorial effect of these components would uncover deeper insights into their functional importance. To address this, we performed systematic analyses of intronic miRNA:host loci in colon cancer. The FTX locus, comprising of a long noncoding RNA FTX and multiple intronic miRNA, was highly upregulated in cancer, and cooperativity within this multicomponent locus promoted cancer growth. FTX interacted with DHX9 and DICER and regulated A-to-I RNA editing and miRNA expression. These results show for the first time that a long noncoding RNA can regulate A-to-I RNA editing, further expanding the functional repertoire of long noncoding RNA. Intronic miR-374b and miR-545 inhibited tumor suppressors PTEN and RIG-I to enhance proto-oncogenic PI3K-AKT signaling. Furthermore, intronic miR-421 may exert an autoregulatory effect on miR-374b and miR-545. Taken together, our data unveil the intricate interplay between intronic miRNA and their host transcripts in the modulation of key signaling pathways and disease progression, adding new perspectives to the functional landscape of multigenic loci.

Significance:

This study illustrates the functional relationships between individual components of multigenic loci in regulating cancer progression.

See related commentary by Calin, p. 1212
Vascular Normalization to Improve Treatment of COVID-19: Lessons from Treatment of Cancer
Authors: Munn Lance L. , Stylianopoulos Triantafyllos , Jain Natalie K. , Hardin C. Corey , Khandekar Melin J. , Jain Rakesh K.
Published: March 2021

The dramatic impact of the COVID-19 pandemic has resulted in an “all hands on deck” approach to find new therapies to improve outcomes in this disease. In addition to causing significant respiratory pathology, infection with SARS-CoV-2 (like infection with other respiratory viruses) directly or indirectly results in abnormal vasculature, which may contribute to hypoxemia. These vascular effects cause significant morbidity and may contribute to mortality from the disease. Given that abnormal vasculature and poor oxygenation are also hallmarks of solid tumors, lessons from the treatment of cancer may help identify drugs that can be repurposed to treat COVID-19. Although the mechanisms that result in vascular abnormalities in COVID-19 are not fully understood, it is possible that there is dysregulation of many of the same angiogenic and thrombotic pathways as seen in patients with cancer. Many anticancer therapeutics, including androgen deprivation therapy (ADT) and immune checkpoint blockers (ICB), result in vascular normalization in addition to their direct effects on tumor cells. Therefore, these therapies, which have been extensively explored in clinical trials of patients with cancer, may have beneficial effects on the vasculature of patients with COVID-19. Furthermore, these drugs may have additional effects on the disease course, as some ADTs may impact viral entry, and ICBs may accelerate T-cell–mediated viral clearance. These insights from the treatment of cancer may be leveraged to abrogate the vascular pathologies found in COVID-19 and other forms of hypoxemic respiratory failure.
Phenotypic Models of CAR T-Cell Activation Elucidate the Pivotal Regulatory Role of CAR Downmodulation
Authors: Greenman Raanan , Pizem Yoav , Haus-Cohen Maya , Horev Guy , Denkberg Galit , Shen-Orr Shai , Rubinstein Jacob , Reiter Yoram
Published: March 2021

Adoptive cell immunotherapy with chimeric antigen receptor (CAR) showed limited potency in solid tumors, despite durable remissions for hematopoietic malignancies. Therefore, an investigation of ways to enhance the efficacy of CARs' antitumor response has been engaged upon. We previously examined the interplay between the biophysical parameters of CAR binding (i.e., affinity, avidity, and antigen density), as regulators of CAR T-cell activity and detected nonmonotonic behaviors of affinity and antigen density and an interrelation between avidity and antigen density. Here, we built an evolving phenotypic model of CAR T-cell regulation, which suggested that receptor downmodulation is a key determinant of CAR T-cell function. We verified this assumption by measuring and manipulating receptor downmodulation and intracellular signaling processes. CAR downmodulation inhibition, via actin polymerization inhibition, but not inhibition of regulatory inhibitory phosphatases, was able to increase CAR T-cell responses. In addition, we documented trogocytosis in CAR T cells that depends on actin polymerization. In summary, our study modeled the parameters that govern CAR T-cell engagement and revealed an underappreciated mechanism of T-cell regulation. These results have a potential to predict and therefore advance the rational design of CAR T cells for adoptive cell treatments.

See related article on p. 872
Abstract PO023: Single-cell transcriptomics identifies gene expression networks driving differentiation and tumorigenesis in the human fallopian tube
Authors: Dinh Huy Q. , Lin Xianzhi , Abbasi Forough , Nameki Robbin , Haro Marcela , Olingy Claire , Chang Heidi , Hernandez Lourdes , Gayther Simon , Wright Kelly N. , Aspuria Paul-Joseph , Karlan Beth , Corona Rosario I. , Li Andrew , Rimel B.J. , Siedhoff Matthew , Medeiros Fabiola , Lawrenson Kate
Published: March 2021

The human fallopian tube harbors the cell-of-origin for the majority of high-grade serous ‘ovarian’ cancers (HGSCs), but its cellular composition, particularly the epithelial component, is poorly characterized. We performed single-cell transcriptomic profiling in 12 primary fallopian specimens from 8 patients, analyzing around 53,000 individual cells to map the major immune, fibroblastic and epithelial cell types present in this organ. We identified 10 epithelial sub-populations, characterized by diverse transcriptional programs including SOX17 (enriched in secretory epithelial cells), TTF3 and RFX3 (enriched in ciliated cells) and NR2F2 (enriched in early, partially differentiated secretory cells). Based on transcriptional signatures, we reconstructed a trajectory whereby secretory cells differentiate into ciliated cells via a RUNX3high intermediate. Computational deconvolution of the cellular composition of advanced HGSCs based on epithelial subset signatures identified the ‘early secretory’ population as a likely precursor state for the majority of HGSCs. The signature of this rare population of cells comprised both epithelial (EPCAM, KRT) and mesenchymal (THY1, ACTA2) features, and was enriched in mesenchymal-type HGSCs, a group known to have particularly poor prognoses. This cellular and molecular compendium of the human fallopian tube in cancer-free women is expected to advance our understanding of the earliest stages of fallopian epithelial neoplasia. Further computational analyses on fibroblast and immune cells together with tumor single-cell data reveals extended contribution of non-epithelial microenvironment and cell-cell interactions and communications in fallopian tube that potentially drive HGSC progression.

Citation Format: Huy Q. Dinh, Xianzhi Lin, Forough Abbasi, Robbin Nameki, Marcela Haro, Claire Olingy, Heidi Chang, Lourdes Hernandez, Simon Gayther, Kelly N. Wright, Paul-Joseph Aspuria, Beth Karlan, Rosario I. Corona, Andrew Li, B.J. Rimel, Matthew Siedhoff, Fabiola Medeiros, Kate Lawrenson. Single-cell transcriptomics identifies gene expression networks driving differentiation and tumorigenesis in the human fallopian tube [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO023.
Abstract IA-05: Deep learning radiomics in cancer imaging
Authors: Hosny Ahmed
Published: March 2021

Radiographic medical images contain a vast wealth of information allowing for accurate non-invasive tumor characterization and ultimately improving cancer care. Radiomics enables the extraction of mineable high-dimensional features from images, quantification of tumor phenotypes for survival, recurrence, and treatment response prediction, and ultimately better patient stratification. Recent advances in AI, deep learning in particular, has allowed for the automated extraction of imaging features without the need for pre-definition. In this talk, we will be exploring deep learning radiomics applications in cancer patients from both single time point and longitudinal imaging data. We will also be identifying challenges regarding the stability, reproducibility, and transparency of such approaches.

Citation Format: Ahmed Hosny. Deep learning radiomics in cancer imaging [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-05.
Abstract IA-07: Radiomics: Transforming standard imaging into mineable data for diagnostic and theragnostic applications
Authors: Lambin Philippe
Published: March 2021

The rise of radiomics, the high-throughput mining of quantitative image features from (standard-of-care) medical imaging for knowledge extraction and application within clinical decision support systems (animation: https://youtu.be/Tq980GEVP0Y) to improve diagnostic, prognostic, and predictive accuracy, has significant and substantial implications for the medical community (1, 2, 5). Radiomic analysis exploits sophisticated image analysis tools and the exponential growth of medical imaging data to develop and validate powerful image-based signatures/models. We will describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making (presently primarily in the care of patients with cancer, however, all imaged patients may benefit from quantitative radiology) (5,8). Finally, the field of radiomics is emerging rapidly; however, the field lacks standardized evaluation of both the scientific integrity and the clinical significance of the numerous published radiomics investigations resulting from this growth. There is a clear and present need for rigorous evaluation criteria and reporting guidelines in order for radiomics to mature as a discipline (see www.radiomics.world). Certain author’s proposed that radiomics could be used as a “virtual biopsy”. It could be the case in the sense that several reports demonstrated that biological features of tumours such as EGFR mutations, HPV status and even hypoxia could be quantified by radiomics (6). There are however two main differences: a) Radiomics is based on the whole tumour in contrast to a biopsy taken most often randomly in an heterogeneous tumour and b) the radiomics values is a continuous variable in contrast to molecular biology assays which are often dichotomized (e.g. mt vs wt). Interestingly, certain radiomics signatures e.g. a proliferation radiomics signature, works as well with cone beam CT which opens the field of “4D-Radiomics” (4, 7). The next step is however a “totalomisc” approach in which radiomics signatures will be used in a multifactorial Decision Support System for both diagnostic or theragnostic questions (3, 9, 10).

Citation Format: Philippe Lambin. Radiomics: Transforming standard imaging into mineable data for diagnostic and theragnostic applications [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-07.
Abstract IA-19: Machine learning and AI in molecular pathology diagnostics and clinical management of cancer
Authors: Snuderl Matija
Published: March 2021

Molecular diagnostics of cancer has undergone a revolution driven by development of high throughput molecular techniques. It is increasingly recognized that malignant tumors previously considered a single entity are in fact composed of multiple different entities that vary in their molecular genetics, underlying biology and most importantly clinical outcome. Integration of omic- and clinical data into practical classification schemes requires novel machine learning approaches to manage big data in a clinically reasonable turnaround time. Accurate molecular classification will make future clinical trials more informative and lead to development of novel therapeutic strategies. While highly informative, advanced molecular testing is expensive and not widely available. We have developed novel methods to predict mutations directly from histopathological slides using artificial intelligence (AI) and image analysis. Mutations in various genes including EGFR, STK11, and BRAF can be predicted with high accuracy by the analysis of the H&E slide image, which is a standard stain in pathology laboratories across the world. Mutational prediction by AI provides a rapid low-cost method to screen patients for potentially targetable mutations in cancer. Cloud based AI mutational predictors could also bring molecular diagnostics to remote or medically underserved areas.

Citation Format: Matija Snuderl. Machine learning and AI in molecular pathology diagnostics and clinical management of cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-19.
Abstract PO-003: Deep learning identifies conserved pan-cancer tumor features
Authors: Noorbakhsh Javad , Farahmand Saman , Foroughi pour Ali , Namburi Sandeep , Caruana Dennis , Rimm David , Soltanieh-ha Mohammad , Zarringhalam Kourosh , Chuang Jeffrey H.
Published: March 2021

Histopathological images are an integral data type for studying cancer. We show pre-trained convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNNs with a common architecture trained on 19 cancer types of The Cancer Genome Atlas (TCGA), analyzing 14459 hematoxylin and eosin scanned frozen tissue images. Our CNNs are based on the Inception-V3 network and classify TCGA pathologist-annotated tumor/normal status of whole slide images in all 19 cancer types with consistently high AUCs (0.995±0.008). Remarkably, CNNs trained on one tissue are effective in others (AUC 0.88±0.11), with classifier relationships recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45±0.16 between classifier pairs on the TCGA test sets. In particular, the TCGA-trained classifiers had average tile-level correlation of 0.52±0.09 and 0.58±0.08 on hold-out TCGA lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) test sets, respectively. These relations are reflected on two external datasets, i.e., LUAD and LUSC whole slide images of Clinical Proteomic Tumor Analysis Consortium. The CNNs trained on TCGA achieved cross-classification AUCs of 0.75±0.12 and 0.73±0.13 on LUAD and LUSC external validation sets, respectively. These CNNs had average tile-level correlations of 0.38±0.09 and 0.39±0.08 on LUAD and LUSC validation sets, respectively. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. This study illustrates pre-trained CNNs can detect tumor features across a wide range of cancers, suggesting presence of pan-cancer tumor features. These shared features allow combining datasets when analyzing small samples to narrow down the parameter search space of CNN models.

Citation Format: Javad Noorbakhsh, Saman Farahmand, Ali Foroughi pour, Sandeep Namburi, Dennis Caruana, David Rimm, Mohammad Soltanieh-ha, Kourosh Zarringhalam, Jeffrey H. Chuang. Deep learning identifies conserved pan-cancer tumor features [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-003.
Abstract PO-004: A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma
Authors: Shirazi Amin Zadeh , McDonnell Mark D. , Fornaciari Eric , Bagherian Narjes Sadat , Scheer Kaitlin G. , Samuel Michael S. , Yaghoobi Mahdi , Ormsby Rebecca J. , Poonnoose Santosh , Tumes Damon , Gomez Guillermo A.
Published: March 2021

Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. Here, we have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading-edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Analysis of segmentation results from tumour images together with matched RNA expression data identified genetic signatures that are specific to these different tumour regions. We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Moreover, in silico cell ontology analysis and single-cell RNA sequencing data from resected glioblastoma tissue samples, showed that these tumour regions had different gene signatures, suggesting they are driven by different cell types in the tumour microenvironment. This points to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Overall, this work identifies key histopathological features that are indicative of patient survival and detected spatially associated genetic signatures that mediate tumour-stroma interactions that should be investigated as new targets in glioblastoma.

Citation Format: Amin Zadeh Shirazi, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon Tumes, Guillermo A. Gomez. A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-004.
Abstract PO-039: Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis
Authors: Wang Yibin , Duggar William N. , Thomas Toms V. , Roberts Paul , Bian Linkan , Wang Haifeng
Published: March 2021

Extracapsular extension (ECE) is a decisive indication for treatment planning of patients with head and neck squamous cell carcinoma (HNSCC). ECE occurs when metastatic tumor cells within the lymph node break through the nodal capsule into surrounding tissues. It is crucial to identify whether ECE occurs in HNSCC patient treatment. Current ECE detection practice mainly relies on the visual identification of doctors and professionals, which can be extremely labor intensive and time-consuming. Therefore, we aim to automatically perform ECE detection using a machine learning technique to classify whether ECE appears or not. In this research, we propose a systematic machine learning approach to detect ECE from 3D computed tomography (CT) scans. The process includes four steps: 1) apply sliding-cube to extract small 3D samples from patient data; 2) various 3D features are extracted for each sample; and 3) three machine learning models, gradient boosting (GB), random forest (RF), and support vector machine (SVM), with feature extraction and selection approaches, are employed for sample classification task; 4) classify the patient based on sample classification result. Different training scenarios are designed for the experimental test. Based on five-fold cross-validation, the experimental results have demonstrated that our model is able to classify ECE and non-ECE patients. To check the explainability of the models, feature analysis is also conducted. The outcome of this research is expected to promote the implementation of artificial intelligence for ECE classification and detection as well as head and neck cancer diagnosis in the radiology computer vision field.

Citation Format: Yibin Wang, William N. Duggar, Toms V. Thomas, Paul Roberts, Linkan Bian, Haifeng Wang. Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-039.
Abstract PO-040: Retinal disease diagnosis through an adaptive deep learning model
Authors: Wang Haifeng
Published: March 2021

This research presents an adaptive deep learning model for retinal disease classification based on 3D optical coherence tomography (OCT) images. Computer-aided retinal disorder diagnosis is one of the critical research topics that can benefit the patient care process. 3D medical images can provide more comprehensive information about retinal structures. However, due to the higher data dimension and more complicated data pattern compared to 2D images, 3D image analysis is challenging the performance of many machine learning models. This research aims to develop a machine learning model for retinal disease diagnosis on 3D OCT images. A public dataset, which includes age-related macular degeneration (AMD), diabetic macular edema (DME), and the normal cases, is used in this study. The model includes three main procedures: 1) Image preprocessing is conducted for each cross-sectional retinal image. A novel Hough transform-based line detection approach is developed to extract 3D retinal layers. 2) 3D retinal layer samples are constructed, which are used to optimize an adaptive deep learning model to identify the most effective and efficient model structure. A novel objective function is proposed to optimize the convergence trend in this process. The constructed deep learning structure is based on the search result of a neural architecture space. 3) The optimized deep learning model is trained using the extracted 3D samples. The experimental results show that the proposed model can significantly boost the training efficiency and improve the testing accuracy.

Citation Format: Haifeng Wang. Retinal disease diagnosis through an adaptive deep learning model [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-040.