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GRANDISSIMO risultato nella lotta al neuroblastoma infantile:

http://www.worldcommunitygrid.org/ab...?articleId=342

20 Feb 2014

Summary
The research team behind the Help Fight Childhood Cancer project has just published a groundbreaking paper. It reveals seven promising drug candidates - identified with the help of World Community Grid members – for neuroblastoma, one of the most common and dangerous forms of childhood cancer.

The Help Fight Childhood Cancer research team at the Chiba Cancer Center in Japan has discovered drug candidates that show great promise as new treatments for neuroblastoma, one of the most common and dangerous forms of childhood cancer. This breakthrough marks one of the most significant scientific discoveries to date for World Community Grid.

Thanks to the contribution of over 200,000 World Community Grid members, the researchers were able to screen three million compounds and identify seven that destroy neuroblastoma tumors in mice without causing any apparent side effects.

The Chiba team plans to partner with a pharmaceutical company for further development, while also expanding their future work on World Community Grid to address other forms of childhood cancer.

In this blog post Dr. Akira Nakagawara, who leads the research team at Chiba, explains the significance of this exciting finding and its potential implications on treatments for other forms of cancer.


Il post citato prima è il seguente:
http://www.worldcommunitygrid.org/ab...?articleId=341

By: Dr. Akira Nakagawara, MD, PhD
President, Chiba Cancer Center

20 Feb 2014

Summary
Blog post announcing a breakthrough in the search for a new treatment for neuroblastoma, written by Dr. Akira Nakagawara, principal investigator for Help Fight Childhood Cancer

Today, thanks to advances in modern medicine, 80% of children diagnosed with cancer are cured. But the prognosis is not nearly as good for children with neuroblastoma, the most common form of cancer diagnosed in infants. Neuroblastoma is a tumor of peripheral nerve tissues that often starts in the adrenal glands and sympathetic ganglia of the neck, chest, or abdomen, and affects approximately one in 8,000 children in the United States and Japan.

More than half of neuroblastoma cases are classified as high risk, and only 30% of these children are cured – a rate that has not improved for two decades. New treatments are urgently needed for this dangerous disease.

Our research team at the Chiba Cancer Center in Japan has been working to develop a new treatment for neuroblastoma. With the help of volunteers participating in the IBM World Community Grid initiative, we have just discovered seven new drug candidates that could potentially be used in new medicines that fight childhood neuroblastoma. These drug candidates work by activating a self-destruct mechanism present in neuroblastoma cancer cells, killing them without affecting healthy cells.

Neuroblastoma cells have a receptor on their surfaces called TrkB. When molecules bind to the TrkB receptor and inhibit its function, a tumor suppressor gene called p53 is activated, causing the neuroblastoma cell to self-destruct in a process called apoptosis. Apoptosis is one of the body's natural processes, and ordinarily helps to eliminate damaged cells before they can form a tumor. However, the TrkB receptor in neuroblastoma suppresses this self-destruct function. A similar TrkB process is involved when many adult cancers, including breast, lung, pancreatic, prostate, and colon cancers, metastasize (i.e. spread beyond an initial site). This means our findings may have implications for treating adult cancers as well.

Our strategy was to search for small molecules that would bind to and inhibit the TrkB receptor on cancer cells. There were millions of potential molecules to examine, making it infeasible to synthesize and test each of them in the laboratory. Instead, we partnered with World Community Grid to create the Help Fight Childhood Cancer project, using computer-based modeling on a massive scale to conduct this search. With the help of over 200,000 volunteers around the world contributing their spare computing power, we screened three million molecules in just two years – a process that would have taken more than 55,000 years on a single computer – and identified seven promising drug candidates for further study.

After additional laboratory testing, we have discovered that the seven drug candidates are very effective at destroying neuroblastoma tumors in mice, even at very low dosages, with no immediately apparent side effects. These results have been published in the peer-reviewed journal Cancer Medicine, available online since January 2014.

Based on these very promising findings, we are now seeking a pharmaceutical partner to collaborate on the further development and testing needed to produce an approved medicine.

This breakthrough was made possible by the support of volunteers around the world who donated their computing power through World Community Grid. On behalf of our research team, I would like to thank World Community Grid volunteers from the bottom of my heart.

Akira Nakagawara, MD, PhD, President, Chiba Cancer Center


Il TGM team ha contribuito con quasi 20 anni di tempo di elaborazione, facendo tornare al progetto 31.000 e passa "lavori". BRAVI (anche) a noi. :rullezza: :rullezza: :rullezza:

No ma sta roba non serve a niente, ci hanno pure chiuso la sezione... :(

Congratulazioni!

Citazione:

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Originariamente Scritto da SaTaN SHaRK

No ma sta roba non serve a niente, ci hanno pure chiuso la sezione... :(

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Grazie al... era diventata un mortorio :bua:

Citazione:

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Originariamente Scritto da LRKKMBVVNT

Congratulazioni!

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Grazie (a nome del team, ovvio) :D

Citazione:

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Originariamente Scritto da SaTaN SHaRK

No ma sta roba non serve a niente, ci hanno pure chiuso la sezione... :(

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Ora che hai portato sfiga chiuderanno pure questo topic! :mad:

Pensavo fosse fuffa ma ho letto l'articolo originale, la cosa sembra davvero interessante.

Ma quale fuffa!
Tieni sempre in considerazione che, per quanto si possa criticare i Militari e gli F35 ed i megaprogetti tipo LHC, oltre la metà della tecnologia che noi usiamo quotidianamente ha origine da ricerche militari o astrusi laboratori di fisica.
Basti pensare ad Internet (nato come sistema per la protezione dei dati), il computer è figlio della "Bomba Polacca" (sistema per decodificare le trasmissioni cifrate dei Nazisti), l'Energia a fissione nucleare (nata con Fermi e figlia del Progetto Manhattan), i Laser e tutti gli usi che ne facciamo oggi (dai lettori DVD alla Chirurgia), gli schermi LCD e LED, persino gli accelerometri che hanno fatto la fortuna di Nintendo erano già presenti nella prima Guerra del Golfo nei caschi dei piloti e servivano per il puntamento delle armi.
Poi la tecnologia viene convertita ed utilizzata in modi creativi.

@PinHead: il WCG è un'assoluta garanzia nel campo. Appoggiano solo ricerche iper-serie.

Inviato dal mio GT-P5100 utilizzando Tapatalk

Nessuno mette in dubbio la serietà con cui si comincia una ricerca, ma spesso i risultati sono pari a zero; in questo caso, ripeto, ho letto l'articolo e mi sono dovuto ricredere. Per "letto" intendo il paper, è il mio campo di studi e per questo ero ipercritico alla solita banfa "cancro sconfitto!!1!!" :asd: Satan non ho capito la tua sparata, lo so meglio di te come funziona la ricerca ma, specialmente in ambito biologico, l'equazione soldi e fatica -> risultati non è MAI vera :asd:

Non era una sparata...semplicemente vedo spesso persone che criticano gli investimenti in ricerca fatti in ambiti come la Fisica, l'Astrofisica o le ricerche militari, quando poi tutti noi ne usufruiamo dei benefici.
P.S. perchè non ti metti anche tu a maciullare un po' di dati?

Perchè ho un pc così obsoleto che ci mette mezzora solo per l'avvio :asd:

Citazione:

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Originariamente Scritto da PinHead81

Perchè ho un pc così obsoleto che ci mette mezzora solo per l'avvio :asd:

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:bua: se vorrai cambiarlo, e se vorrai contribuire col calcolo distribuito... :asd: facci un fischio che ti consigliamo noi l'hardware giusto :D (no, non riceviamo provvigioni dagli store di hardware :asd: )

Ecco stasera mi è impazzito il PC e pure Boinc!

Up per altre due news importanti:

http://www.worldcommunitygrid.org/ab...?articleId=368

By: Help Conquer Cancer research team
9 Jul 2014

Summary
The research team expands to advance their analysis of the millions of protein-crystallization images processed by World Community Grid volunteers. This will help scientists understand how protein structure can lead to better cancer drug design.


Dear World Community Grid volunteers,

Since you completed your calculations for Help Conquer Cancer (HCC) in 2013, we have begun analyzing the results you generated. Here, we provide an update on that analysis work as next steps to publish our findings and make the data publicly available.

Analyzing Results

Biologists and medical researchers use the three-dimensional (3D) structure of proteins to design drugs and understand protein function. Solving a protein's 3D structure requires a long and difficult sequence of steps. The protein needs to be made into a pure crystal (like you might do to crystalize sugar by slowly evaporating sugar water.) Then X-rays are shown through the crystal, and the neat array of protein molecules in the crystal creates a pattern on the film which can be analyzed mathematically to ascertain the structure of each protein molecule. Unlike sugar, protein is notoriously difficult to crystallize. HCC addressed this bottleneck in the pipeline: with a method for recognizing successfully formed protein crystals in images taken from a very large number of automated experimental attempts. For HCC, World Community Grid volunteers analyzed hundreds of millions of these images, but these results need to be processed further in order to generate reliable automatic image classifiers, discover trends in data, and ultimately improve our understanding how proteins form crystals. Our analysis work is in progress, and there are some exciting results we will be reporting on next time.

Additionally, over the last year we have devoted considerable energy and resources to our new project on World Community Grid - Mapping Cancer Markers (MCM), and other cancer-gene-signature projects that our research group is involved in. To help with both priorities and directions, our team expanded and we have a new Post-Doctoral Fellow (Dr. Lisa Yan) helping us with advancing our HCC research.

Publishing our results and findings

We have not yet decided the time-frame or the exact form of how we will make the HCC data you generated available to the public. Thanks to World Community Grid volunteers, our project's terabytes of raw image data have been transformed into terabytes of computed image features (morphological image properties used in automated image classification). The identity of proteins in the crystallization trials is largely unknown to us and partially unknown even to the Hauptman Woodward Institute (HWI), the source of the images. The features we have computed do not directly relate to crystallization outcomes or human-understandable image labels. A classifier is required to translate computed features to meaningful human labels or experimental outcomes. We have trained multiple image classifiers so far, but are confident that we can improve them. It is essential (and practical) that we finish this part of research, and publish our findings before releasing the useful data.

Paper publications

The Grid-computed results of Help Conquer Cancer have yet to be fully analyzed. Once complete, we intend to publish one or more papers based on the analysis, but cannot currently estimate a time-frame.

Collaborations

The High-Throughput Screening Lab at HWI supplied the original protein-crystallization image data, and indeed continues to generate more. Both HWI and the scientists who send them protein samples will benefit from the HCC research in two ways: better systems for automatically classifying protein-crystallization images (saving time and manual labour), and better understanding of the protein crystallization process.

E

http://www.worldcommunitygrid.org/ab...?articleId=369


By: The Mapping Cancer Markers research team
10 Jul 2014

Summary
The lead researcher for Mapping Cancer Markers presents a roadmap for the project to analyze signatures for 4 types of cancer: lung, ovarian, prostate and sarcoma; an update on his team’s progress thus far, and an invitation to join the research team in an August cancer fundraiser.


On behalf of the Mapping Cancer Markers team, we want to start by saying thank you! In just 7 months, World Community Grid members have donated over 60,000 years of processing time to support our research. As a result, we are nearly done with the “benchmarking” portion of the project, which determines the characteristics of our search space. Over the coming months and years, we will pursue more targeted approaches to discover relevant gene signatures. Today we want to give you both a high-level roadmap and some further detail about what is happening with the project.

Project roadmap

The project is anticipated to run for two years, and we plan to analyze signatures for 4 different types of cancer. At the moment, we're enlisting your help to process research tasks for lung cancer, and will move on to ovarian cancer, prostate cancer and sarcoma.

Currently, the Mapping Cancer Markers project has two phases:
In the first phase we have been attempting to set a benchmark for further experiments.
The second phase will be geared towards finding clinically useful molecular signatures, initially focusing on gene signatures that can predict the occurrence of various types of cancer.
We expect a smooth transition between the two phases, with no interruption in work. The “benchmarking” phase of our project is important not only for our own research, but for other researchers around the world. Every year, numerous groups worldwide develop and publish interesting molecular signatures for various diseases, including multiple cancers. One of the challenges of interpreting these findings is that many of the reports are not directly comparable to each other. The benchmarking phase of our project is designed to set a standard benchmark so that we and other groups can estimate how well individual signatures perform.

You can think of this benchmarking phase as a bit like designing an IQ test. By establishing a standard test and scoring system, we can evaluate any person's intelligence. The results from the first phase of Mapping Cancer Markers will allow us to create such a test for existing and future gene signatures, so that we can tell which ones have the best predictive ability.

Benchmarking

Our preliminary analysis of the work units processed so far (roughly 26 billion gene signatures) is focused on the nature of genes in the signatures, measuring their quality by assessing how accurately they contribute to identifying patients with poor prognosis. On the analytics side, we have also been evaluating the use of a software package to aid with post-processing our results.

One of the goals of the first project phase is to understand if some genes might have better predictive ability than others. To do this, we took the top 0.1% of the gene signatures and identified the individual genes that make up each signature. For each gene, we looked at how many times it occurred within top scoring signatures and plotted the scores of those signatures (see figure below). The blue line shows the average of all of the genes together. The red line highlights the worst-performing single gene while the green line indicates our best-performing gene. The average of all the genes is very similar to the worst single gene. This is not surprising, because most genes are likely to have poor predictive ability. However, we are looking for the few genes that stand out from the field. In other words, if we have 1 million potential gene signatures, and we look at the top 1,000 scoring signatures, we can find groups of genes such as the one shown in green, which have better predictive ability.


This information is important because if we know which genes have the best predictive ability, it may help us and other researchers to evaluate the value of other signatures: if an unknown signature has one of the top genes in it, it is likely to be a useful signature for identifying, assessing, predicting or treating a disease.

As a side note, this benchmarking process is why members may have experienced shorter or longer than usual runtimes over the past several months. The core algorithm of the Mapping Cancer Markers engine, used to evaluate each potential gene signature, has a processing time that is highly dependent on the statistical characteristics of each signature. The search space targeted by a single work unit can sometimes contain time-consuming signatures, which together lead to a longer total runtime. This also means variability with the size of Mapping Cancer Markers results. A typical work unit will evaluate tens of thousands of potential gene signatures, many of which are of low quality. Signatures below a certain quality threshold are removed from the returned results. However, the search space targeted by a single work unit can sometimes contain a high proportion of high-quality gene signatures. If this happens, the result file is larger than usual.

Funding & Fundraising

We’re happy to report that there are several potential sources for further funding. Applications are in progress with the Ontario Research Fund, the Canada Foundation for Innovation, and the US Department of Defense. Of course, the free computing power provided by World Community Grid volunteers is absolutely essential to our research. However, additional funding will help us to both leverage contributions from volunteers, and fully utilize findings of the Mapping Cancer Markers computations, with a primary focus on lung and ovarian cancer.

Finally, if you will be in Ontario between 15-17 August, please consider donating to, or cheering on the Team Ian Ride from Kingston to Montreal, which raises money for the Ian Lawson Van Toch Cancer Informatics Fund at the Princess Margaret Cancer Centre (if you are interested, please contact us about joining the Team Ian ride this or next year). If you can join us, it will give you the chance to meet some of the research team, as well as raise money for a worthy cause and participate in an outstanding event. For more details visit: http://www.team-ian.org/

:rullezza:

Io c'ero!

Me too :rullezza:

http://www.thestar.com/news/insight/...ter_power.html

Bene. Più se ne parla, meglio è :sisi: