Evidence map›Paper›PMID 26523938›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2016

Integrating Cell Phone Imaging with Magnetic Levitation (i-LEV) for Label-Free Blood Analysis at the Point-of-Living.

Murat Baday, Semih Calamak, Naside Gozde Durmus, Ronald W Davis, Lars M Steinmetz, Utkan Demirci

Abstract read
In one paragraph

Article in Small (Weinheim an der Bergstrasse, Germany), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
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  9. Article
  10. Sensing a revolution.Molecular systems biology · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Murat Baday *Canary Center at Stanford for Cancer Early Detection, Radiology Department, School of Medicine, Stanford University, CA, USA, 94304.
Semih Calamak *Canary Center at Stanford for Cancer Early Detection, Radiology Department, School of Medicine, Stanford University, CA, USA, 94304.
Naside Gozde DurmusDepartment of Biochemistry, School of Medicine, Stanford University, CA, USA, 94304.
Ronald W DavisDepartment of Biochemistry, School of Medicine, Stanford University, CA, USA, 94304.
Lars M SteinmetzStanford Genome Technology Center, Stanford University, CA, USA, 94304.
Utkan DemirciCanary Center at Stanford for Cancer Early Detection, Radiology Department, School of Medicine, Stanford University, CA, USA, 94304.

Funding

WHOLE YEAST GENOME TRANSCRIPTIONAL AND FUNCTIONAL GENE MAPS--A FEASIBILITY STUDYP01HG000205 · NHGRI · STANFORD UNIVERSITY · PI DAVIS, RONALD WAYNE, HOWE, ROGER THOMAS · 1990 to 2016
$120.9M
Modulation of erythrocyte function by complementR01HL096795 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI GHIRAN, IONITA CALIN · 2011 to 2014
$1.7M
Novel disposable microchips for HIV-1 viral loadR01AI093282 · NIAID · STANFORD UNIVERSITY · PI DEMIRCI, UTKAN · 2012 to 2015
$1.7M
Minimizing the role of cryoprotectant toxicity for cryopreservationR01EB015776 · NIBIB · STANFORD UNIVERSITY · PI DEMIRCI, UTKAN · 2012 to 2015
$1.5M
Nano-Acoustic Masonry of Microgels for Bottom-Up Tissue EngineeringR21HL112114 · NHLBI · PENNSYLVANIA STATE UNIVERSITY, THE · PI DEMIRCI, UTKAN, DEMIREL, MELIK · 2012 to 2013
$452k
Malaria screening in resource-poor settings using a simple, power-free, cell phone-friendly deviceR21TW009915 · FIC · BETH ISRAEL DEACONESS MEDICAL CENTER · PI GHIRAN, IONITA CALIN · 2014 to 2015
$435k
FIC NIH HHS R21 TW009915FIC NIH HHS R21TW009915NHGRI NIH HHS P01 HG000205NHLBI NIH HHS R01 HL096795NHLBI NIH HHS R21 HL112114NHLBI NIH HHS R21HL112114NIAID NIH HHS R01 AI093282NIBIB NIH HHS R01 EB015776NIBIB NIH HHS R01EB015776-01A1
6 · The paper itself

Abstract

There is an emerging need for portable, robust, inexpensive, and easy-to-use disease diagnosis and prognosis monitoring platforms to share health information at the point-of-living, including clinical and home settings. Recent advances in digital health technologies have improved early diagnosis, drug treatment, and personalized medicine. Smartphones with high-resolution cameras and high data processing power enable intriguing biomedical applications when integrated with diagnostic devices. Further, these devices have immense potential to contribute to public health in resource-limited settings where there is a particular need for portable, rapid, label-free, easy-to-use, and affordable biomedical devices to diagnose and continuously monitor patients for precision medicine, especially those suffering from rare diseases, such as sickle cell anemia, thalassemia, and chronic fatigue syndrome. Here, a magnetic levitation-based diagnosis system is presented in which different cell types (i.e., white and red blood cells) are levitated in a magnetic gradient and separated due to their unique densities. Moreover, an easy-to-use, smartphone incorporated levitation system for cell analysis is introduced. Using our portable imaging magnetic levitation (i-LEV) system, it is shown that white and red blood cells can be identified and cell numbers can be quantified without using any labels. In addition, cells levitated in i-LEV can be distinguished at single-cell resolution, potentially enabling diagnosis and monitoring, as well as clinical and research applications.

Indexed as

Cell PhoneMagneticsBlood Cell CountErythrocytesHumansImaging, Three-DimensionalLeukocytesSingle-Cell Analysisblood countdiagnosticsmagnetic levitationpoint-of-care deviceprecision medicine, blood analysis

Identifiers

PMID26523938
PMCPMC4775401

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.