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https://read.qxmd.com/read/30776176/high-level-extracellular-production-of-recombinant-human-interferon-alpha-2b-in-glycoengineered-pichia-pastoris-culture-medium-optimization-high-cell-density-cultivation-and-biological-characterization
#1
Srikanth Katla, Bappa Karmakar, Subbi Rami Reddy Tadi, Naresh Mohan, B Anand, Uttariya Pal, Senthilkumar Sivaprakasam
AIMS: The present study was aimed at design of experiments (DoE) and artificial intelligence based culture medium optimization for high level extracellular production of a novel recombinant human interferon alpha 2b (huIFNα2b) in glycoengineered Pichia pastoris and its characterization. METHODS AND RESULTS: Artificial neural network- Genetic algorithm (ANN-GA) model exhibited improved huIFNα2b production and better predictability compared to RSM. The optimized medium exhibited 5-fold increase in huIFNα2b titer compared to the complex medium...
February 18, 2019: Journal of Applied Microbiology
https://read.qxmd.com/read/30772062/-the-age-of-artificial-intelligence-in-lung-cancer-pathology-between-hope-gloom-and-perspectives
#2
Simon Heeke, Hervé Delingette, Youta Fanjat, Elodie Long-Mira, Sandra Lassalle, Véronique Hofman, Jonathan Benzaquen, Charles-Hugo Marquette, Paul Hofman, Marius Ilié
Histopathology is the fundamental tool of pathology used for more than a century to establish the final diagnosis of lung cancer. In addition, the phenotypic data contained in the histological images reflects the overall effect of molecular alterations on the behavior of cancer cells and provides a practical visual reading of the aggressiveness of the disease. However, the human evaluation of the histological images is sometimes subjective and may lack reproducibility. Therefore, computational analysis of histological imaging using so-called "artificial intelligence" (AI) approaches has recently received considerable attention to improve this diagnostic accuracy...
February 13, 2019: Annales de Pathologie
https://read.qxmd.com/read/30745156/early-access-to-health-products-in-france-major-advances-of-the-french-conseil-strat%C3%A3-gique-des-industries-de-sant%C3%A3-csis-to-be-implemented-modalities-regulations-funding
#3
Nicolas Albin, Frédéric Chassagnol, Jean-François Bergmann
In a context of perpetual evolution of treatments, access to therapeutic innovation is a major challenge for patients and the various players involved in the procedures of access to medicines. The revolutions in genomic and personalized medicine, artificial intelligence and biotechnology will transform the medicine of tomorrow and the organization of our health system. It is therefore fundamental that France prepares for these changes and supports the development of its companies in these new areas. The recent "Conseil stratégique des industries de santé" launched by Matignon makes it possible to propose a regulatory arsenal conducive to the implementation and diffusion of therapeutic innovations...
December 13, 2018: Thérapie
https://read.qxmd.com/read/30721962/artificial-intelligence-for-breast-cancer-imaging-the-new-frontier
#4
Christoph I Lee, Joann G Elmore
No abstract text is available yet for this article.
February 5, 2019: Journal of the National Cancer Institute
https://read.qxmd.com/read/30720861/artificial-intelligence-in-cancer-imaging-clinical-challenges-and-applications
#5
REVIEW
Wenya Linda Bi, Ahmed Hosny, Matthew B Schabath, Maryellen L Giger, Nicolai J Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F Dunn, Raymond H Mak, Rulla M Tamimi, Clare M Tempany, Charles Swanton, Udo Hoffmann, Lawrence H Schwartz, Robert J Gillies, Raymond Y Huang, Hugo J W L Aerts
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses...
February 5, 2019: CA: a Cancer Journal for Clinicians
https://read.qxmd.com/read/30714125/potential-roles-of-artificial-intelligence-learning-and-faecal-immunochemical-testing-for-prioritisation-of-colonoscopy-in-anaemia
#6
Ruth M Ayling, Stephen J Lewis, Finbarr Cotter
Iron deficiency anaemia (IDA) is the most common cause of anaemia and a frequent indication for colonoscopy, although the prevalence of colorectal cancer (CRC) in IDA is low. Measurement of faecal haemoglobin by immunochemical techniques (FIT) is used to detect symptomatic patients. We studied FIT in patients with anaemia attending a gastroenterology clinic in Plymouth and looked at an artificial intelligence (AI) learning algorithm (ColonFlag™) in these patients, together with a cohort who had undergone colonoscopy for IDA in London...
February 3, 2019: British Journal of Haematology
https://read.qxmd.com/read/30707177/artificial-intelligence-system-of-faster-region-based-convolutional-neural-network-surpassing-senior-radiologists-in-evaluation-of-metastatic-lymph-nodes-of-rectal-cancer
#7
Lei Ding, Guang-Wei Liu, Bao-Chun Zhao, Yun-Peng Zhou, Shuai Li, Zheng-Dong Zhang, Yu-Ting Guo, Ai-Qin Li, Yun Lu, Hong-Wei Yao, Wei-Tang Yuan, Gui-Ying Wang, Dian-Liang Zhang, Lei Wang
BACKGROUND: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. METHODS: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN...
January 30, 2019: Chinese Medical Journal
https://read.qxmd.com/read/30704213/-high-definition-mri-rectal-lymph-node-aided-diagnostic-system-based-on-deep-neural-network
#8
Y P Zhou, S Li, X X Zhang, Z D Zhang, Y X Gao, L Ding, Y Lu
Objective: To investigate the clinical significance of high definition (HD) MRI rectal lymph node aided diagnostic system based on deep neural network. Methods: The research selected 301 patients with rectal cancer who underwent pelvic HD MRI and reported pelvic lymph node metastasis from July 2016 to December 2017 in Affiliated Hospital of Qingdao University. According to the chronological order, the first 201 cases were used as learning group. The remaining 100 cases were used as verification group. There were 149 males (74...
February 1, 2019: Zhonghua Wai Ke za Zhi [Chinese Journal of Surgery]
https://read.qxmd.com/read/30704208/-cold-thinking-in-the-boom-of-artificial-intelligence
#9
Z F Jiang, F Li, F R Xu
Artificial intelligence clinical decision-support system is an important direction of artificial intelligence in the medical field. Both international and domestic researchers are exploring the application value of intelligent decision-making system in the field of cancer. But at the same time of the craze, there are still some problems in the intelligent decision-making system. Combining the work of the research groups in this field, this paper explores the current confusions and solutions, and hopes to help clinicians better understand intelligent decision-making...
February 1, 2019: Zhonghua Wai Ke za Zhi [Chinese Journal of Surgery]
https://read.qxmd.com/read/30703787/-looking-back-2018-focused-on-colorectal-cancer
#10
Jian Cai, Lei Wang
Colorectal cancer is one of the most common malignant tumors, and its incidence and mortality are increasing year by year in China. In 2018, for the first time, the FIT-DNA test was written into the expert consensus as the recommended screening technology in China. As the core technology of colorectal cancer screening, colonoscopy for right colon cancer is further supported. With the application of artificial intelligence technology in colonoscopy, the efficiency and accuracy of screening will be greatly improved...
January 25, 2019: Zhonghua Wei Chang Wai Ke za Zhi, Chinese Journal of Gastrointestinal Surgery
https://read.qxmd.com/read/30684706/machine-learning-in-neuro-oncology-can-data-analysis-from-5-346-patients-change-decision-making-paradigms
#11
REVIEW
Christopher A Sarkiss, Isabelle M Germano
BACKGROUND: Machine learning (ML) is an application of artificial intelligence (AI) giving computer systems the ability to learn data, without being explicitly programmed. ML is currently successfully used for optical character recognition, spam filtering, and face recognition. The aim of this study is to review its current application in the field of neuro-oncology. METHODS: We conducted a systematic literature review on PubMed and Cochrane Database using a keyword search for the period January 30, 2000-March 31, 2018...
January 23, 2019: World Neurosurgery
https://read.qxmd.com/read/30671672/translating-cancer-genomics-into-precision-medicine-with-artificial-intelligence-applications-challenges-and-future-perspectives
#12
REVIEW
Jia Xu, Pengwei Yang, Shang Xue, Bhuvan Sharma, Marta Sanchez-Martin, Fang Wang, Kirk A Beaty, Elinor Dehan, Baiju Parikh
In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine...
January 22, 2019: Human Genetics
https://read.qxmd.com/read/30667332/emerging-applications-of-artificial-intelligence-in-neuro-oncology
#13
Jeffrey D Rudie, Andreas M Rauschecker, R Nick Bryan, Christos Davatzikos, Suyash Mohan
Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice...
January 22, 2019: Radiology
https://read.qxmd.com/read/30655050/detection-of-extraprostatic-extension-of-cancer-on-biparametric-mri-combining-texture-analysis-and-machine-learning-preliminary-results
#14
Arnaldo Stanzione, Renato Cuocolo, Sirio Cocozza, Valeria Romeo, Francesco Persico, Ferdinando Fusco, Nicola Longo, Arturo Brunetti, Massimo Imbriaco
RATIONALE AND OBJECTIVES: Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pretreatment local staging is performed with MRI, while the gold standard is represented by histopathological analysis after radical prostatectomy. Texture analysis (TA) is a quantitative postprocessing method for data extraction, while machine learning (ML) employs artificial intelligence algorithms for data classification. Purpose of this study was to assess whether ML algorithms could predict histopathological EPE using TA features extracted from unenhanced MR images...
January 14, 2019: Academic Radiology
https://read.qxmd.com/read/30653684/hyperspectral-imaging-in-automated-digital-dermoscopy-screening-for-melanoma
#15
Anna-Marie Hosking, Brandon J Coakley, Dorothy Chang, Faezeh Talebi-Liasi, Samantha Lish, Sung Won Lee, Amanda M Zong, Ian Moore, James Browning, Steven L Jacques, James G Krueger, Kristen M Kelly, Kenneth G Linden, Daniel S Gareau
OBJECTIVES: Early melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy...
January 17, 2019: Lasers in Surgery and Medicine
https://read.qxmd.com/read/30652604/diagnostic-classification-of-cystoscopic-images-using-deep-convolutional-neural-networks
#16
Okyaz Eminaga, Nurettin Eminaga, Axel Semjonow, Bernhard Breil
PURPOSE: The recognition of cystoscopic findings remains challenging for young colleagues and depends on the examiner's skills. Computer-aided diagnosis tools using feature extraction and deep learning show promise as instruments to perform diagnostic classification. MATERIALS AND METHODS: Our study considered 479 patient cases that represented 44 urologic findings. Image color was linearly normalized and was equalized by applying contrast-limited adaptive histogram equalization...
December 2018: JCO Clinical Cancer Informatics
https://read.qxmd.com/read/30652580/clinical-integration-of-digital-solutions-in-health-care-an-overview-of-the-current-landscape-of-digital-technologies-in-cancer-care
#17
Shivank Garg, Noelle L Williams, Andrew Ip, Adam P Dicker
Digital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. In this review, we focus on the landscape of the current integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices, digital patient information collection, telehealth, and digital assistants...
December 2018: JCO clinical cancer informatics
https://read.qxmd.com/read/30644411/blood-biochemistry-analysis-to-detect-smoking-status-and-quantify-accelerated-aging-in-smokers
#18
Polina Mamoshina, Kirill Kochetov, Franco Cortese, Anna Kovalchuk, Alexander Aliper, Evgeny Putin, Morten Scheibye-Knudsen, Charles R Cantor, Neil M Skjodt, Olga Kovalchuk, Alex Zhavoronkov
There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels...
January 15, 2019: Scientific Reports
https://read.qxmd.com/read/30630221/overview-of-deep-learning-in-gastrointestinal-endoscopy
#19
REVIEW
Jun Ki Min, Min Seob Kwak, Jae Myung Cha
Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deep-learning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy...
January 11, 2019: Gut and Liver
https://read.qxmd.com/read/30630127/-artificial-intelligence-for-cancer-genomic-medicine-understanding-cancer-is-beyond-human-ability
#20
Satoru Miyano
Cancer is a very complex disease that is caused by mutations in genomes and evolves spatiotemporally in a patient. Our institute implemented IBM Watson for Genomics and realized a turnaround time for patient diagnosis of less than four days that includes whole genome sequencing and interpretation of the data.
January 2019: Brain and Nerve, Shinkei Kenkyū No Shinpo
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