Comments (8)

1. 

   Moderator Comment

   Chris Mullins

   I feel this is an important observation and relevant to many fields. Perhaps it may be useful to in time (perhaps in Phase II of the KRND) outline some approaches to addressing how best this data may be assessed for true in vivo relevance - e.g., is there a analogous "large scale" approach that may be taken to move the analysis of candidates forward?

   2 years ago

   0 Vote up

   0 Vote down
2. 

   Robert Hurst

   Perhaps we should concern ourselves more with how to analyze these "mountains of data" in clinically meaningful ways as well as how to efficiently integrate small and big science. These are not mutually exclusive and ideally should work together much more efficiently than is the case currently.

   2 years ago

   0 Vote up

   0 Vote down
3. 

   Jeremy Duffield

   I agree with the sentiments of Dennis Brown. Part of the problem I think is that basic sciences require another level of commitment from people compared with joining a group who mine datasets that are already established. The dwindling number of US trained fellows combined with training grants that exclude people without citizenship is not helping this issue, and the lack of longer-term commitment to talented fellows who travel down the basic science path combined with the increasing salary gap between basic science fellows compared with those taking other Nephrology career avenues serves to undermine the base of talent that wants to enroll in basic sciences.

   2 years ago

   0 Vote up

   0 Vote down
4. 

   Pablo Ortiz

   I totally agree. While it is great to have mountains of data suggesting "possible" pathways that may be the product or the cause of hypertension, CKD, or other renal disease, translating these data into meaningful renal function is a lot harder. The "small science" or targeted discovery pathway is time consuming and requires the development of new techniques and refinement of existing ones. Few young scientists are being appropriately trained in these techniques and even fewer are pursuing new avenues to monitor renal function at the cellular and molecular level. Without these, I find it hard to effectively translate the mountains of data into RENAL FUNCTION.

   2 years ago

   0 Vote up

   0 Vote down
5. 

   Hsiao Lai

   As one of those young scientists, I would agree with all of the above comments.

   2 years ago

   0 Vote up

   0 Vote down
6. 

   Sumant Chugh

   I agree with Dennis. Generation of mountains of data is not helpful if resources to mine the data are not available. Generating a large dataset may only require 3-4 million dollars in funding, but mining it requires additional investigators who will need another 200 million dollars of funding, which is next to impossible in the current environment. Most investigators anyway would independently confirm any data obtained from a large data base by conducting their own limited study before embarking on a project that may last upwards of 5 years.

   2 years ago

   0 Vote up

   0 Vote down
7. 

   Pierre Ronco

   I think that omics and targeted discovery pathways are neither mutually exclusive nor antagonistic provided that the population of patients be very well phenotyped. Recent advances in the pathophysiology of membranous nephropathy nicely illustrate the complementarity of these approaches. I fully agree with Dennis that omics applied to large cohorts of heterogeneous patients are loss of money and waste of time, particularly for young investigators.

   2 years ago

   0 Vote up

   0 Vote down
8. 

   James George

   I also agree with the above sentiments. It is important to remember that the majority of innovations come from small laboratories working on problems that may or may not be "in fashion" at the time. In addition, to divert large proportions of funding to a few large enterprises will, in my opinion, result in a diminution of the overall research base.

   2 years ago

   0 Vote up

   0 Vote down