sidestream dark field (sdf+) imaging microscan (MicroVision Medical)
90
Structured Review
MicroVision Medical
sidestream dark field (sdf+) imaging microscan
Sidestream Dark Field (Sdf+) Imaging Microscan, supplied by MicroVision Medical, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sdf+imaging/sidestream+dark+field+device+microscan/pm40217945-59-5-11
Average 90 stars, based on 1 article reviews
Sidestream Dark Field (Sdf+) Imaging Microscan, supplied by MicroVision Medical, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sdf+imaging/sidestream+dark+field+device+microscan/pm40217945-59-5-11
Average 90 stars, based on 1 article reviews
sidestream dark field (sdf+) imaging microscan - by Bioz Stars,
2026-09
90/100 stars
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Related Articles
Imaging:Article Title: Effects of fluid and norepinephrine resuscitation in a sheep model of endotoxin shock and acute kidney injury. Article Snippet: .. C. Ince has developed Article Title: Real-time observation of microcirculatory leukocytes in patients undergoing major liver resection Article Snippet: .. Prof. Dr. Ince has developed Article Title: Artificial intelligence in intensive care: moving towards clinical decision support systems Article Snippet: The high complexity of care in the Intensive Care Unit environment has led, in the last decades, to a big effort in term of the improvement of patient’s monitoring devices, increase of diagnostic and therapeutic opportunities, and development of electronic health records.. Such advancements have enabled an increasing availability of large amounts of data that were supposed to provide more insight and understanding regarding pathophysiological processes and patient’s prognosis providing useful tools able to support physicians in the clinical decision-making process.. On the contrary, the interpolation, analysis, and interpretation of a such big amount of data has soon proven to be much more complicated than expected, opening the way for the development of tools based on machine learning (ML) algorithms. Article Title: Intra-renal microcirculatory alterations on non-traumatic hemorrhagic shock induced acute kidney injury in pigs. Article Snippet: Funding This study was financially supported by an Innovation Grant of the Dutch Kidney Foundation (14 OI 11) and NanoNextNL, a micro- and nanotechnology consortium of the Government of the Netherlands. .. Statements and declarations Financial interest Dr. Can Ince runs an Internet site microcirculationacademy.org that offers services (e.g., training, courses, analysis) related to clinical microcirculation and has received honoraria and independent research grants from Fresenius-Kabi, Baxter Health Care, and AM-Pharma; has developed Article Title: Association between serosal intestinal microcirculation and blood pressure during major abdominal surgery Article Snippet: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: C.I. has received honoraria and independent research grants from Fresenius-Kabi, Bad Homburg, Germany; La Jolla Pharmaceutical Co., La Jolla, CA, USA; and Cytosorbents Monmouth, NJ, USA. .. C.I. has developed Article Title: Increased Hepatic Microvascular Density, Oxygenation, and VEGF in the Hypertrophic Lobe following Portal Vein Embolization in Rabbits. Article Snippet: .. Conflict of Interest Statement C.I. has developed Article Title: Microcirculatory Response to Blood vs. Crystalloid Cardioplegia During Coronary Artery Bypass Grafting With Cardiopulmonary Bypass Article Snippet: .. CI has developed Article Title: Noninvasive, in vivo assessment of the cervical microcirculation using incident dark field imaging. Article Snippet: .. Declaration of competing interest Prof. Ince has developed Microscopy:Article Title: Artificial intelligence in intensive care: moving towards clinical decision support systems Article Snippet: The high complexity of care in the Intensive Care Unit environment has led, in the last decades, to a big effort in term of the improvement of patient’s monitoring devices, increase of diagnostic and therapeutic opportunities, and development of electronic health records.. Such advancements have enabled an increasing availability of large amounts of data that were supposed to provide more insight and understanding regarding pathophysiological processes and patient’s prognosis providing useful tools able to support physicians in the clinical decision-making process.. On the contrary, the interpolation, analysis, and interpretation of a such big amount of data has soon proven to be much more complicated than expected, opening the way for the development of tools based on machine learning (ML) algorithms. |