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BioSocieties ◽  
2021 ◽  
Author(s):  
Zainab Afshan Sheikh ◽  
Ayo Wahlberg

AbstractWhile unequally resourced partners from the so-called global South are often considered ‘mere sample providers’ in larger international genomics collaborations, in this paper, we show how they strategically work to mobilize their role in a global system of tissue exchange to deliver services for local communities. We unpack how a prenatal diagnostic service for thalassemia in Pakistan emerged out of the maneuvering efforts of internationally connected Pakistani researchers. By tracing the distributed capacities that emerged and circulated as they set about improving medical genetics in Pakistan, we outline some key conditions that led to the establishment of the service: first, the scale of unmet needs that geneticists faced when collecting data as part of their research that made medical genomics a relevant field; secondly, joint efforts between researchers and physicians that were engaged with the challenge of decreasing disease prevalence through diagnostics and abortion; and finally, the ways in which international research collaborations helped generate resources to improve medical genetics in Pakistan. To understand how genetic research and medicine is currently being developed in Pakistan, we need to ethnographically re-center our analyses in ways that allow us to identify the resourceful ways in which researchers maneuvre to secure locally relevant outcomes.


Author(s):  
Ramzi Nasir ◽  
Pam Czerniewska ◽  
Sara Pearlman ◽  
Geetha Nagendran ◽  
Christine Jenkins ◽  
...  
Keyword(s):  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mélina Rivard ◽  
Coulombe Patrick ◽  
Catherine Mello ◽  
Diane Morin ◽  
Marjorie Morin

Abstract Background This paper aimed to describe the diagnostic service trajectory of families of children with autism or intellectual disability in the province of Québec and identify predictors of parents’ perceptions of its quality. Methods The Evaluation of the services Trajectory in Autism by Parents instrument was completed by 259 parents at an assessment clinic. Children’s clinical records were also examined. Results On average 26 months elapsed between their first concerns and their child’s diagnosis, a period during which few (25%) received support. Parents’ evaluations were generally positive but were lower for the accessibility of the pre-assessment phase and the flexibility of the assessment process. Longer delays and a greater number of professionals consulted were associated with lower quality ratings. Some language-, immigration status-, and income-related differences in families’ appraisals were noted. Conclusion The diagnostic trajectory for neurodevelopmental disorders within public services in Québec presents some efficiency and accessibility challenges. Possible improvements are proposed to facilitate screening and to support families throughout this phase of their trajectory.


2021 ◽  
Author(s):  
Tarun Bansal ◽  
Suraj Santosh Agrawal ◽  
Deepak Kumar ◽  
M T Shambu ◽  
P Inbarajan
Keyword(s):  

Lung Cancer ◽  
2021 ◽  
Vol 156 ◽  
pp. S17-S18
Author(s):  
Sue-Lis Yeo ◽  
Syed Muneer-ahmed ◽  
Mohammed Haris ◽  
Muhammad Ganaie ◽  
Nadeem Maddekar ◽  
...  

2021 ◽  
Vol 185 ◽  
pp. 96-107
Author(s):  
Shannon G.M. Kirejczyk ◽  
Chloe Goodwin ◽  
Zoltan S. Gyimesi ◽  
Trevor T. Zachariah ◽  
Ginger L. Sturgeon ◽  
...  

2021 ◽  
Vol 7 ◽  
Author(s):  
Seong K. Mun ◽  
Kenneth H. Wong ◽  
Shih-Chung B. Lo ◽  
Yanni Li ◽  
Shijir Bayarsaikhan

Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.


2021 ◽  
pp. 519-541
Author(s):  
Xiaoning Liu ◽  
Yifeng Zheng ◽  
Xingliang Yuan ◽  
Xun Yi

2020 ◽  
Vol 191 (3) ◽  
Author(s):  
Elizabeth Soyode ◽  
William Pritchard ◽  
Megan Hayman ◽  
Annie Davenport ◽  
Pasupathy Sivasothy ◽  
...  

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