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CFP Narrative Conference (April 7-10, 2011)full name / name of organization: International Society for the Study of Narrative (ISSN) contact email: email@example.com, email@example.com The 2011 Narrative Conference is sponsored by Washington University in St. Louis and the International Society for the Study of Narrative and will be held in St. Louis, Missouri, April 7-10, 2011. The Narrative Conference is an interdisciplinary forum addressing all dimensions of narrative theory and practice. We welcome proposals for papers and panels on all aspects of narrative in any genre, period, discipline, language, and medium. Deadline for receipt of proposals: October 30, 2010. Proposals for individual papers Proposals for panels Please send proposals by email in PDF, Word, or WordPerfect to: firstname.lastname@example.com. For further information please contact Emma Kafalenos at firstname.lastname@example.com or Erin McGlothlin at email@example.com or visit http://narrative.wustl.edu. cfp categories: cultural_studies_and_historical_approaches gender_studies_and_sexuality humanities_computing_and_the_internet interdisciplinary international_conferences theory


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ANVSOFT Inc. is a software development company with expertise and experience in digital photo, digital video, VCD and DVD making applications. It also provides multimedia software and firmware solutions for personal computer, mobile device and consumer electronics manufacturers. Our creative and talented team designed Photo DVD Maker, DVD Burning Xpress, iPod Photo Slideshow, iPod Movie Maker which enable users of all levels to easily and effectively edit, enhance, and manage their digital photos and videos and enjoy their creative works on computer, TV and mobile devices such as mobile phones, iPods, PDAs, PSPs.
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Jenn Savedge
Author of parenting books blogs about raising children and health issues.
Safety check: Is your swing set ready for summer?
We've got you covered with a checklist to help make sure your backyard play equipment is safe for the kids.
Mon, Apr 01, 2013 at 02:00 PM
Photo: WhiteHouse.gov
Believe it or not, warmer days are on their way. And that means that it's time to wipe the dirt — and snow — off of your backyard play equipment and make sure that it's ready for another season of use. Winter can be rough on all parts of swing sets — plus slides, monkey bars, climbing walls, forts, and all sorts of backyard play gear. So, now is a good time to give all of your outdoor play equipment a once-over, to size up any repairs that may need to be made and make sure that everything is safe for your kids to use.
This safety-proofing checklist was sent to me by Swing-N-Slide, and I thought it would be a good one to share. Here's how to make sure that your backyard play equipment is ready for play:
Before they play...
Check all metal parts for rust or other deterioration Provide your children with a soft landing – proper surfacing is key Replace any deteriorated parts with new parts Lubricate metallic moving parts Check all protective coverings on bolts, pipes, edges and corners — and replace if loose, cracked or missing Tighten all hardware Cut off all protruding threaded ends of bolts and other fasteners and remove any sharp edges Ensure equipment is on level ground, no less than 6 feet from any structure or obstruction such as a fence, garage or tree Adjust all swings for a minimum 8-inch clearance between the swing and the ground surface Use a water seal on your gym set to protect wood and prevent cracking and warpingDuring play season...
Check all nuts and bolts twice monthly for tightness and tighten as required. Oil all metallic moving parts monthly Check all hardware and equipment for sharp edges twice monthly Check swing seats and chains monthly during the usage season for evidence of deterioration Remind your children of safe play rules including appropriate swinging space, sharing with others and being polite to other children
Are your kids ready to hit the swing set?
Related toy safety story on MNN: Toy Safety: Were these toys ever a good idea?
The opinions expressed by MNN Bloggers and those providing comments are theirs alone, and do not reflect the opinions of MNN.com. While we have reviewed their content to make sure it complies with our Terms and Conditions, MNN is not responsible for the accuracy of any of their information.


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Tavaris Jermell Slaughter
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Smartphone accessory maker BCoda showed Jak at CES 2013, a USB dongle representing an emerging category of tech: a Wi-Fi dongle designed to stream media.
The Targus Jak Messenger is one of the least convenient notebook bags we've reviewed; if you're looking for a messenger bag, look to more functional and stylish options, such as the Belkin NE-07 notebook bag or the Mobile Edge Premium Messenger.
There's 400 percent more light in intergalactic space than sources could produce, say researchers. Could dark matter be the cause?
Sony has announced a streaming service called PlayStation Now that will allow you to play a back catalog of PlayStation titles on your PS4, PS3, Vita and Bravia TV.
Sony's PS4 event left us asking plenty of questions. Which games are PS4 exclusives? Which are PS3 games? And which are both?
From the new Wii U console to a souped up chair to park yourself in, here's our round-up of the best gaming gifts.
Sony invested a generous chunk of time in laying out the fine details for the PlayStation 3's motion control system, PlayStation Move, today. We were also treated to list of compatible Move titles, as well as the debut of some new games built specifically for the platform as well.
Gran Turismo, launching October 1 with the PSP Go, was the headlining game at the E3 Sony press conference, but there were plenty of others.
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Thread: Commercial Truck Ext.
03-02-1999, 05:06 PM #1SCH79Firehouse.com Guest
Commercial Truck Ext.
I'm looking for any available info on commercial truck extrication, rescue safety etc. to aid in putting together a class for my Dept.
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Our county training academy has hosted several commercial vehicle classes. Most of our instructers came from Montgomery cty Md.
Excellent class. try www.mfri.com
Maryland fire rescure inst for info.
Tom Carr was our lead instructer.
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I am Dr. Vikram Chauhan, MD – (Ayurveda) from India and practicing in Chandigarh from last 15 years. I strongly believe that nature has given us immense healing power. I am running my own Ayurveda healing center & website WW.PLANETAYURVEDA.COM The Clinic Location is-Plot No. 627, Sec.82 JLPL Indl.Area, Mohali -Punjab INDIA ( Near Chandigarh).E mail – firstname.lastname@example.com or Call my assistant - Ashwani Thakur at 0091-9915593604 OR +91-172-521-4030
Friday, January 2, 2009
Herbs to lower Creatinine - Decoction
The remedy is -
Take 5 gm of each of 5 herbs -
Mutrakrichantak Churna ( a herbal formulation for kidneys) + Tribulus powder (Gokhru Powder) + Chicory leaves (Kaasni Leaves) + 5 Grass roots (Trinpanchmool-5 diuretics) + Ficus religiosa bark ( Pipal tree bark)
Take 5 gm of each of the herbs, put this mixture into about 200 ml of water, boil until the water remains about 50 ml , consume once daily .
For quicker results, add Rencure Formula 2 capsules twice daily & Varunadi vati
This should help in reducing the urea and creatinine levels.
Along with this, you can also drink Arjun Tea - 1 cup once daily.
Check http://www.planetayurveda.com for further information or contact at email@example.com
56 comments:
Dear sir,
My father is diabetic patient and his serum creatinine level was 9 earlier and high urea, doctor suggested permanent dialysis(twice/week). After first session of dialysis (4 time) it was brought down to 6 (It is growing).
Can you pls advice to lower the s.cre.,
we are from West Bengal, is the herb available in Kolkata, West Bengal.
Thank you.
Subrata Bauri- AnonymousJune 12, 2010 at 7:44 AM
where is your clinic located ? does those herbs at one point liminate the dialysis process
ReplyDelete hi, My clinic is located in Chandigarh. These herbs do not initiate dialysis rather they help in lowering down urea and creatinine levels and help in decreasing the frequency of dialysis required.
Dialysis is a temporary solution to keep the dangerous levels of toxins down so that they do not cause any permanent damage to the system. Dialysis has its own importance. If the urea and creatinine levels go beyond certain limits, then dialysis is recommended but if these herbs are used along with the dialysis process, slowly and steadily the need for dialysis reduces.
thanksHi Ramasha ! I think for 12 % kidney function, you should continue dialysis as well as start using herbs. The herbs are not going to lower down urea and creatinine immediately. So dialysis can not be avoided immediately. She may have to continue these herbs along with the dialysis. The dosage of herbs should also be less. The levels of urea and creatinine should also be monitored regularly. The herbs act slowly.
ThanksI would recommend a combination of herbs like those in the formulation - Mutrakrichantak Churna and Rencure formula.
Those who do not have access to these above 2 formulations can use - (decoction/herbal tea/infusion/juice etc.) of herbs like Corn Silk, Ficus Religiosa bark (pipal tree bark) , Neem tree bark, Tribulus (Gokhru) terrestris whole plant, Chicory (Kasasni), are also good.
The products Rencure formula and Mutrakrichantak Churna which contain most of these herbs are available at my site - www.planetayurveda.com
but I would again say that do not expect Miracles to happen overnight. The herbs do take time. You have to continue the dialysis and remain under supervision of your nephrologist for monitoring urea,creatinine levels and dialysis.hi dear sir
My mother's in kidney problem now she creatinine 4.75 & urea 62 now she already taking nature therapy & whet grass . before this remedy my mother creatinine was 5.31 & urea 98 .Would you please please advice me some remedy. What are the steps we need to take. Please make it urgent to response. waiting ur replyVineet, The herbs to lower creatinine are described above in the post I have written. Please read. I am glad that the creatinine level has decreased but I am not against use of Wheat grass juice...but I would still recommend you to kindly keep a record of potassium levels as wheat grass is rich in potassium and usually in kidney failure patients Potassium in high. Same is the case with Aloe vera juice. I don't recommend Aloe vera juice to kidney failure patients and people who have high creatinine levels.
You can continue with Wheatgrass juice if the potassium level is ok. This helps in lowering down urea and creatinine levels.
Other herbs which you can use can be read at http://www.planetayurveda.com/mutrakrichantak.htm
Thanks- AnonymousAugust 16, 2010 at 6:36 PM
Hi, Dear sir
I have a problem with my kidney. A month ago my creatinine level was 4.2 and the urea nitrogen level was 32. As of today my creatinine has increased to 5.0 and urea nitrogen 12. I am on a no salt diet for the past two months, and eating food with low protein,potassium and phosporus. My kidney problem is due to high blood pressure but its under control now below 130 and 80. Can the herbal remedy you discribed above be consumed toghether with the high blood pressure medication that I am on? I have also heard of flax seed oil and tulsi seed. Can they help reduce the creatinine levels? Thanks Creatinine levels come down if the blood pressure remains under control for a long period of time. So take care of your blood pressure first. It should not go high. Specific herbs to lower creatinine levels are those in mutrakrichantak churna. I don't recommend tulsi adn flax seed to lower down creatinine levels as according to ayurveda , they are hot in nature especially Tulsi. So tulsi is not recommended for blood pressure patients.
Cooling herbs like Rose petals, Arjuna, Phyllanthus niruri, Pipal are recommended - please read about Detox tea at - http://www.arjuntea.com This tea contains most of these ingredients.
The other herbal combination is mutrakrichantak churna- AnonymousAugust 17, 2010 at 9:28 AM
Dr Vikram,
Thank you for your prompt reply .
You mentioned about mutrakrichantak churna .Can it be consumed with my blood pressure medication ?Yesterday my doctor had prescribed sodium bicarbonate in addition to my other medication .Are they any restrictions that I should be aware of if I do consumed the herb that you prescribed ?
One last question , how about cinnamon powder and caconut water ? I believe they are good to lower blood pressure but would they lower the creatinine level too ? As fas as I know coconut is rich in potassium .
Thank you. Sodium in any form will cause water retention, so I don't it should be recommended. Yes, Mutrakrichantak churna can be consumed with your blood pressure medication or if someone suffering from diabetes can also have this with the medications being taken. This herbal blend contains herbs which work as natural diuretics and kidney supporting. They do not have any interaction with other medicines rather they aid the kidney function.
There are no specific restrictions along with the mutrakrichantak churna or Rencure formula I recommended.
Cinnamon is good for high cholesterol and it is not really specific for blood pressure. Traditionally Cinnamon is used to lower triglycerides levels and clear the blocked arteries. Arjuna is the herb of choice for lowering down blood pressure. We have used Cinnamon, Arjuna and Phyllanthus niruri which is another wonderful herb for liver, kidneys and circulatory system in Detox tea. (check www.arjuntea.com) So you can consume arjun tea or detox tea once to twice daily if you have blood pressure, high cholesterol and also suffer kidney failure.
now about coconut water- yes, it is rich in potassium. It should not be technically as it is rich in potassium and the potassium levels are already high in kidney failure, but it has been observed that the renal failure responds to coconut water. So it should be used in a very tricky way. If the potassium levels are not so high, then one can recommend Coconut water. If there is not much water retention, then one can consume coconut water instead of regular water.
if the kidney failure is at initial stage and the urea and creatinine levels are not so high then Coconut water is a miracle.
So it depends how you use it.- AnonymousAugust 21, 2010 at 4:42 PM
Dear Dr. Vikram: Thanks for recommending mutrakrichantak churna for my kidney problems. I am on none salt diet. Even with high creatinine (5.0) and buns reading (45), is it advisable to take yougurt although its high in protein.
ReplyDelete For High urea and Creatinine levels, high protein diet is not recommended. Sometimes due to kidney failure there is low albumin in the blood. there is loss of proteins from the kidneys and the blood protein levels are low. Then some protein intake is recommended.
Since urea and creatinine are end products of protein metabolism, therefore too much consumption of Protein diet is not recommended.
According to Ayurvedic medicine, these ammonia based end products like urea and creatinine are called "ama" or endotoxins. These are formed more due to heavy diet like Yougrt and other heavy, greasy, sticky foods like Banana, Non-vegetarian diet, eggs etc.
So according to Ayurveda- yogurt should not be consumed as it produces more Ama and can further increase in Urea and creatiine.
According to modern medicine, yogurt is rich in proteins, it can not be given to high urea and creatinine patients as the end product of protein metabolism is formation of urea and creatinine. If the protein levels are low in blood, then it can be given in small quantity.- AnonymousAugust 25, 2010 at 12:44 AM
Hello Doctor,
This is Sathya and am 26 years old.
I found that my kidney has problem before 2 months. And am continue with the dialysis for the past two months. When i got admitted in the hospital my creatinine was 15.3 and now it's 7.6 & urea is 96. What measure should i need to take for reducing the urea, creatinine level? Kindly confirm quickly Hello Sathya, Please read the blog post. I have posted some herbal remedies to lower urea and creatinine levels and they are helpful. Dialysis has to be done as your creatinine levels is 15.3 before dialysis. You can use Ayurvedic remedy called Mutrakrichantak Churna , Rencure formula, Varunadi Vati to lower urea and creatinine levels. They assist kidneys in a natural manner. Thanks
Dr. vikram- AnonymousAugust 26, 2010 at 10:20 PM
I started using soy milk in my coffee latte's and I just had my creatinine level checked and it is 2.0. Could the protein in the soy milk cause the level to go up?
ReplyDelete - Ruchir JainSeptember 21, 2010 at 9:58 PM
Hello Doctor,
This is Ruchir. Your blog is very informative and helpful. My father has creatinine level of 5.3 and has a varying blood pressure i.e. sometimes it beacomes normal and sometimes a bit high ranging 140/100 and is NOT diabetic. I read somewhere that Cinnamon ("Dal chini") is one of the herb that can reduce the creatinine level, is that so?
Also, the formula that have you mentioned in your post, is that available with you or we need to create it by ourselves or is readily available in the ayurvedic medical stores?
Will appreciate your prompt reply.
Ruchir Jain - AnonymousSeptember 21, 2010 at 10:17 PM
Hello Doctor,
I'm Ruchir Jain. My father is having the creatinine level of 5.3 and is NOT diabetic. He is also having a varying blood pressure i.e. some times its 80/120 and sometimes its 100/140-150.
I read somewhere that Cinnamon (dal chini) is also a useful herb in reducing creatinine level, is it true?
Also i wanted to ask that the formula you mentioned in the post is readily available in the Ayurvedic medical Stores or i need to get in touch with you regarding the same?
Will appreciate your early reply.
Ruchir Jain Hello Ruchir,
All the supplements are available at our website - http://www.planetayurveda.com/mutrakrichantak.htm
and if you need others also, we have all that in our clinic. You can contact Mr. Ashwani at 0091-9876643604 to get these and check the pricing.
Chronic Blood pressure may be the reason for high creatinine levels as it puts pressure on kidneys as well. Cinnamon reduces cholesterol levels and can be useful in reducing creatinine levels but it is not really recommended alone for high blood pressure. It is ok for cholesterol reduction.
It should be used in combination with other herbs like Arjuna for blood pressure.
Read about this at - http://www.planetayurveda.com/arjuntea1.htm
The herbs I have mentioned are not really readily available at all ayurvedic medical stores as some of them are rare. So be in touch with Mr. Ashwani.
Thanks !Dear Dr.
I am having problem of deep vein thrombosis with pulmonery hypertension. I am on allopathic medication from six years and now due to side effects of these medicine my s. creatinine & ureric acid levels are increasing day by day. Request you to suggest ayurdedic/herbal redmedies on these problems. My age is 39 ears and I have this problem from six years.
VinayYes, some medications do increase creatinine levels and also uric acid levels. You should consult your doctor and discuss this and bring this into his notice so that he can reduce the dosage or stop the medicine.
If you want to know some Ayurvedic remedies to lower down urea and creatinine, then I would recommend some herbal blends like Mutrakrichantak Churna, Rencure formula, varunadi Vati and Punarnava mandur. You can read about them at www.planetayurveda.com
There are various herbs to lower down creatinine and urea levels and work naturally in this problem.- AnonymousSeptember 29, 2010 at 7:03 AM
Dr Vikram
I would like to thank you for recommeding the M Churna for my creatinine level ..I have consumed it for few weeks and my report shows that my creatinine level has decreased from 5.16 to 4.4 .I am still on low protein no salt diet , however my hemoglobin has decreased too from 10.8 to 8.8 .I am wondering if you can suggest anything else .I have checked online about iron supplement with Vit B12 and Folic acid if this would help to increase red blood count ? Thank you Yes, The Mutrakrichantak Churna or the M churna is an effective ayurvedic remedy to lower creatinine levels and help kidney failure. To increase haemoglobin, it's ok to consume vit. B 12 and folic acid. You can also consume beet root juice- 10 - 20 ml daily and tablet punarnava mandur to improve Hb.
The other kidney failure herbal remedies include mutrakrichantak churna, rencure fomrula, varunadi vati and punarnava mandur.Dear Sir,
Thanks a lot for your reply. As per your advice, I have consulted with cardiologist on dosage of medicine (dilzem 30, dytor 10). As per him, RA & RV dialated and PH is near about 75mmgh so at rather than decreasing dose, it should be increased.He has prescribed new medicine Bosantas which is very costly and I can not afford as well as side effects are also high.
Sir, further to add, I had jaundice last month (SGPT is normal but S.Billirubin total is 2 (direct .7 and indirect 1.3)as per latest report done on 28th. My hb is very high at 17.
I am very consfused and not able to understand what is to be done. Request you to suggest -
1) Is there any ayurvedic / home remedies on pulmonery hypertension so that I can stop allopathic medicines.
2) I am on anticoagulation and taking warf 5 mg. Due to that hb level is increasing day by day, is any substite availalbe for this in ayurvedic.
3)I have taken jaundex (sandu pharma) for S. Billirubin and it came to 1.7 from 2.4 but again it is increased to 2. What will be reason of the same?
I have seen www.planetayurveda.com as suggested by you. Website is really very good and content are absolutly excellent. Really like planet. Thank you very much for suggesting.
Thanks & Regards,
VinayDear vinay ji,
Warf does not increases the Hb, it is used as blood thinner. Jaundex is good for S.biliribun but I would recommend you Yakrit Plihantak churna - you can read about this one at - http://www.planetayurveda.com/yakrit.htm
For pulmonary hypertension, I would recommend Arjun tea, Punarnava Mandur tablets. you can read about these at planet ayurveda site.
If you want to get these, you can go to contact us page of planetayurveda site and contact the manager for information about how to get them.
Do not stop any allopathic medicines at present. Just continue for a while.untill you get response from these ayurvedic medicines
Thanks- AnonymousSeptember 30, 2010 at 9:52 AM
Thank you Dr Vikram for your reply .
So I could take M Churna with punarnava mandur tablet to increase my HG in addition to Vit B12 and Folic Acid .But how about iron supplement ?Shall I consume that too . However I have been instructed by my Dr to have EPO Therapy ( some injection on weekly basis) to increase my red blood cell ct.
Thank you The Erythropoietin - check at http://en.wikipedia.org/wiki/Erythropoietin is a kind of hormone secreted by kidneys which helps to make blood. In case of kidney failure, there is drop in hemoglobin because this hormone is not available at all. So the Erythropoietin injections or EPO therapy is recommended on weekly basis.
So this is ok and you should undergo this therapy. This is specific only to increase blood count and does not affect renal failure in any way.
Vit. B12 also helps in blood formation and so does Iron supplements.
Our main focus should be on reviving the kidney failure. If the kidneys are working fine, then the body will start making blood on its own. The mutrakrichantak churna and punarnava mandur helps in this condition. Punarnava mandur also helps in improving the hemoglobin.
Along with Mutrakrichantak Churna and Punarnava mandur tablets, I would recommend you Varunadi Vati, Arjun tea , Yakrit Plihantak CHurna and Rencure Formula. You can get detailed information about all these at - www.planetayurveda.com or write to firstname.lastname@example.com- Anil KumarOctober 5, 2010 at 11:17 PM
Dear All,
Is there any body who is benifitted from Dr Chauhan's medicines for kidney problems. Many homeopathic doctors are claiming the same way, but no one has come up to say that he is benifitted. Dear Anil, I have published your comment so that people can read this one too. I have put all this on my blog so that more and more people can know about Ayurvedic remedies which have potential to lower down creatinine levels and urea levels. The herbs can be procured from us as well as from any nearby ayurvedic store. I also recommend people to be in touch with your nephrologist or an Ayurvedist nearby.
A qualified Ayurvedic doctor will definitely agree with me that these herbs if used in a proper manner will help the patients.
I do not doubt the intention of even those homeopathy doctors who are claiming the same way. we must put try to take things positively by trying whatever may be out there in nature which can be very helpful to mankind.
Thanks - Dr. Vikram- AnonymousOctober 8, 2010 at 12:33 PM
Hi Dr Vikram
The punarnava mandur tablets that u suggested , that can increase my hemoglobin level together with M Churna can I get from your clinic in Chandigarh since I ll be travelling fr US to India in a couple of days time , or do I have to get them online ? Thanks - AnonymousOctober 12, 2010 at 9:20 PM
I want to purchase Your Medicine which will help to reduce Creatinine & Uria level in blood.
How to purchase these medicine ?? Kindly reply.
Note : I have described my Father's problem in previous post. - BhariniOctober 16, 2010 at 12:24 PM
Dear Dr Vikram,
my husband is on hemodialyses after the first kidney transplant failure and now waiting for the second transplant, the first lasted for 6 years before failed.what ayurvedic medicine he should take in order to reduce the dialyses time and frequency - now 3 times per week 4-5 hours every time. very strong weakness after the dialyses every time, very frequently insomnia, somtimes could sleep only one hour, could ayurveda help with that as well? thank you so much I would suggest you to start the kidney supporting herbs described at www.planetayurveda.com
The herbal supplements for this problem are mutrakrichantak, rencure formula, varunadi vati etc.
These should not be a immediate and complete replacement for renal transplant but these should help reduce creatinine levels and support kidneys normal function.- AnonymousOctober 19, 2010 at 6:27 AM
How long can one start seeing the result of your arjun tea in lowering high blood pressure and the other herbs for the creatinine? - AnonymousDecember 3, 2010 at 12:00 AM
hi dr Vikram,
im reschel from the Philippines. we just found out that my mother had a kidney disease and now she is diagnosed end stage of renal disease. She was not yet advise on dialysis. we tried already other alternative way like drinking juice of lemon grass, corn hair,green tea, and a Chinese medicine recommended by our alternative med Dr. the kidney wash. we were surpriced instead her creatinine will lower than but it this was already her highest ever 750.21. im so worried dr. is immunocal whey protein isolate good for my mom. i have also no idea where to find those herbal plants here in the Philippines. - AnonymousDecember 17, 2012 at 10:57 AM
Sir my name is Ritwiz
Mrs.Archana Shrivastava the Subject of this case and my mother is a Hypertension patient from around 25+ yrs, and was detected for diabetes from last 15+ years. In year 2007-2008 Subject was detected suffering from "Minimal Change Disease" treated from steroids for around 2 years. From Jan 2012 her cretanine level started to show fluctuating treads which is in general "Increasing". Mean while the subject was admitted 4 to 5 tyms for the following treatment under hear "CKD" cronic kidney disease and was managed with Inj 20% albumin, Inj Neorecormon 500mg, IV SC, Inj. Venofer, Inj Dytor and other supportive measures and was discharged with following advice.
Advice on Discharge
*Amaryl 2mg once daily
*Glarintus 20mg once daily [ insuline inj]
*Thyroxine 150mg
*Revelamer 800 2 tab thrice a day
*Amaryl 2mg once daily
*Ecosprin AV, Ciploric, Zevit, Matilda Plus A/D
*Zytanix 5mg 2/7
*Dytor 20mg 5/7
*Alpha-D3 0.5mg OD
*Moxcent 0.3 mg OD
*Pan D twice a day
As per her latest report
Phosphate [ as phosphorus]5.8mg/dl ref point = 2.5-4.5mg/dl
Uria nitrogen(BUN) 44 mg/dl ref point = 7-25mg/dl
Creatinine 5.18 mg/dl
Gfr estimated is 9 mL/min/1.73m2
Doctor says that probably in 3 to 6 months they have to send her for dialysis and we dont want that to happen
Docters says there is no treatment in alternative medicine as well. Can you suggest what should we do in this case - AnonymousJanuary 28, 2013 at 4:56 AM
hello sir,
my dad is 47 years old, he is suffering with high BP, and his serum creatine level is 6.2, and urea level is 87, we are using ayurvedic medicine, we bought gokhru powder so my ayurvedic physician is telling that creatine, and urea level may go up if we travel also, or we have cold, constipation, like problems, kindly advice how to use gokhru because i belive in ayurveda because when my father started ayurvedic treatment his serum creatine was 6.0, and it came to 5.5, when he was taking rest in the house, it raised when he started travelling. Dear Doctor,
Initially my dad's creatinine level is 10 and he is on dialysis for past 3 months, later we found out he has blocks in heart, so we did bypass and now he is on dialysis, his creatinine level is 5 now, urea 50 and potassium 5.5, his fluid intake is 750 ml and urine output is 1 ltr/day. he is not having any symptoms of CKD, active nw, can u suggest me what can i do further


text: The essays in The Beauty and Glory of the Holy Spirit both exalt and revel in the third person of the Trinity. Through an assortment of studies - categorized according to their biblical, doctrinal, historical, or pastoral focus - this book sets before readers the inestimable ministry of the blessed Holy Spirit. Contributors include David Murray, Geoffrey Thomas, John Thackway, Malcolm Watts, Gerald Bilkes, Michael Barrett, John Carrick, George Knight, Morton Smith, Ian Hamilton, William Shishko, William VanDoodewaard, Joel R. Beeke, Joseph Morecraft, Ryan McGraw, and Joseph Pipa. Type: Hardcover () Category: Books > Theology ISBN / UPC: 9781601781840/1601781849 Publish Date: 7/1/2012 Item No: 308021 Vendor: Reformation Heritage Books |


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See definition in
Oxford Advanced Learner's Dictionary
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ca|boo¦dlePronunciation: /kəˈbuːd(ə)l/
(also kaboodle)
Definition of caboodle in English:
(in phrase the whole caboodle or the whole kit and caboodle) informal The whole number or quantity of people or things in question: sort out your best snaps, complete the entry form, and pop the whole caboodle into an envelope addressed to us once you get them talking about one case, they might as well settle the whole kit and caboodle
More example sentences
- Ah yes, I love cartoons, animation, the whole kit and caboodle.
- Yes, it's a truly woeful collection that for some reason my mother decided to ship over to me here, the whole kit and caboodle.
- The first floor is accessory heaven, bags, sunglasses, scarves, the whole kit and caboodle.
Mid 19th century (originally US): perhaps from the phrase kit and boodle, in the same (see kit1, boodle).
The caboodle in the expression the whole caboodle or the whole kit and caboodle, meaning 'the whole lot', is a mysterious word. The phrase was originally the whole kit, and later the whole kit and boodle. Boodle meant 'a lot, a crowd', or 'money', and may be connected to Dutch boedel, meaning 'possessions'. See also kit
Words that rhyme with caboodle
boodle, canoodle, doodle, feudal, noodle, poodle, strudel, udalDefinition of caboodle in:
- US English dictionary
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search the archive
search the archive |
Sidney at Kalamazoo, 12-15 May 2011; Deadline 15 Sept. 2011full name / name of organization: International Sidney Society contact email: firstname.lastname@example.com The Sidney Society will sponsor two open sessions on Philip Sidney and his Circle at the 46th International Congress on Medieval Studies Western Michigan University (Kalamazoo, Michigan). The conference website is here: http://www.wmich.edu/medieval/congress/ 12-15 May 2011 Abstracts are invited on any subject dealing with Philip Sidney and his circle. As always, we encourage proposals from newcomers as well as established scholars. Papers should be limited to twenty minutes in reading time. Please do not submit an abstract to two different sessions of the conference in the same year. Abstracts (250-500 words) should be submitted electronically and should indicate clearly your mailing address and phone number. If you need special equipment for the talk (digital projector, etc.), let us know when you submit your abstract, rather than later, please. Deadline for abstracts: 15 September 2010. Please send your abstracts (email preferably) to: Joel B Davis cfp categories: bibliography_and_history_of_the_book gender_studies_and_sexuality general_announcements poetry religion renaissance


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Storkcraft - Beatrice Dresser and Storage Unit, Choose Your Finish
About this item
The Storkcraft Beatrice Dresser and Storage Unit makes a practical addition to your baby's nursery. While the solid wood construction ensures durability, the non-toxic finish makes it a safe choice. Featuring a three-drawer chest with a raised cabinet on the side, this Storkcraft dresser offers enough space to store everyday baby essentials. These include clothes, diapers and even toys. The adjustable shelf adds to the convenience. The smooth metal glide system allows the drawers to move in and out without making much noise. You can use the lower area as a changing station for your child. Designed with a cam-lock construction, assembling this solid wood baby dresser is simple and easy. This piece of decoration is an ideal complement to other Storkcraft Beatrice nursery furniture items.
While requiring some assembly, this simple piece is a snap to put together and is made out of durable solid wood, veneers and wood products. Most importantly, it gives you a convenient place to keep the various items that you need to store away. This Storkcraft Beatrice Dresser and Storage Unit is an ideal addition to any nursery.
Storkcraft Beatrice Dresser and Storage Unit:
- Made of solid wood, veneers and wood products
- Storkcraft dresser features cam-lock construction for easy assembly
- All drawers come with a smooth metal glide system
- Beautiful and non-toxic finish
- Storkcraft dresser and storage unit requires assembly
- All Storkcraft products meet or exceed U.S. and Canadian standards and come with a 1-year warranty
Collection: | Beatrice |
Primary Color: | Brown |
Multi Pack Indicator: | No |
Model No.: | 03585-749 |
Shipping Weight (in pounds): | 108.0 |
Product in Inches (L x W x H): | 48.0 x 18.0 x 37.25 |
Assembled in Country of Origin: | USA or Imported |
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Walmart No.: | 2689112 |
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Soaps Boards :: The Young and the Restless Forum :: Who's Chelsea's greatest victim??????
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Kendrick1994 |
Adam, Billy, Dylan or her unborn child???? |
greengrass |
Quote Kendrick1994: Adam, Billy, Dylan or her unborn child???? Dylan |
Calmie |
What? Doesn't make sense. What are you talking about. |
Katoh |
Her unborn child has no voice. Plenty of people have a baby and marry someone besides the bio father. The baby has two fathers that give twice the love and attention.. Not this babe...she will be pulled from side to side and not know the facts of her birth for who knows how long..This is all because of Chelsea... |
On a Clear Day |
Quote Kendrick1994: Adam, Billy, Dylan or her unborn child???? If Billy had not gotten out of that hole in the ground, dark pit of a jail cell in Myanmar where nobody knew where he disappeared to, I would have said he was the greatest victim. Darn, that was cold to leave a man to rot for life. Next in line, if not for Fridays U.S. scenes where Dylan bore his grieving soul about PTSD and losing that little kid, Ara, I would have said Chelsea herself was her biggest victim. She can't get out of her own way to have a good life and make smart decisions. But after Friday's show I would have to say definitely Dylan is the biggest victim because he is already fragile and trying to integrate back into post war society so the betrayal by a loved one and the kid not being his will blow him to smithereans because he's hanging on by a thread grasping at anybody who will love him who he can form some sort of normal life with. Adam is a victim too but he's a tough guy and will probably find out the kid is his someday so he's not quite as bad of a mark to me. |
zuzu61 |
Quote On a Clear Day: Quote LucyT: Y&R! Y&R FANS!! -------------- BILLY!! & 2 little children!! |
Quote greengrass: Quote Kendrick1994: Adam, Billy, Dylan or her unborn child???? Dylan Her unborn baby,Dylan,Adam.Billy.OMG she will pay for her dirty deeds soon.I hope. |
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Friday, September 8, 2006
Twisted has 3000+ unit tests. That is not the mo...
Twisted has 3000+ unit tests. That is not the most tests of any project I know, but dang it is a bunch.
3 comments:
- AnonymousSeptember 12, 2006 at 2:58 AM
I'm also amazed by the amount of unit tests in feedparser project which definitely must have smaller code base than twisted. It brings some confidential for the module, for sure.
ReplyDelete - jcalderoneSeptember 12, 2006 at 9:04 AM
Hmmm, not to detract from FeedParser's test coverage, but even though the author calls what it has "unit tests", I think they are more like acceptance tests. A unit test exercises a single unit. FeedParser's tests parse complete sample inputs, exercising much more code than a single unit at a time.
Still, that's an awesome amount of tests, particularly, as you say, for such a small code base. It speaks both to the attentiveness of the author and the amazing range of complexity and confusion around XML and RSS. :)
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Mary always liked a boy in here class named Chase. She would always try to confess her love but always chocked up. She didn't know why. She was just about to give up hope when...
"Huh?" Mary was startled when a flash of light filled her room. Before her stood a silver haired girl with white angel wings who smiled before her.
"Who are you?" Mary asked.
"I'm here to help you with your dilemma. You wish to confess to your true love but can't." The angel spoke.
"Your right I do. I like him so much. I wish he was the ideal person for me and I was the ideal person for him." Mary spoke as a light flashed in her face.
"It is done. Just meet up with him tomorrow and your wish will be filled, farewell." The angel disappeared into thin air leaving Mary excited. So she called Chase and asked him if he would like to go to the beach with her tomorrow and he gladly accepted.
That night Mary couldn't sleep and by the next day they were on the beach having a great time.
"I don't know but I never realized how cute you were Mary." Chase said pulling her in closer.
"Oh, thanks Chase. I really like you too." Mary said when she noticed something about Chase was changing.
His hair had become softer and orange while his face had become small and petite. She blushed a bit while the rest of his body became feminine as his chest produced two C-Cup sized boobs. His hip and butt plumped up with the rest of his fat while his arms and legs lost all of their muscle but were left slender and smooth.
"Ohhhh~, I feel different Mary. Whats happening?" Chase asked while his swim trunks formed a white two piece bathing suit over his new body.
"I don't know. You're becoming a girl! I thought I said ideal person. Why is he becoming a she?" Mary thought to herself feeling a little faint herself now. She looked over to see Chase now fully changed lying in the sand on the ground. Her face was so beautiful Mary began to blush.
"Huh? She's so cute. My heart is racing. Why? I feel different as well." Mary thought to herself.
"Hey Mary, could you help me up please?" Chase asked her extending her now smooth and long arms.
"S-sure." Mary said helping her up when Chase pulled her in for a kiss.
"Mmmmmm~" Mary said pulling away quickly. "What are you doing?" She asked.
"Whats wrong Mary? We kiss all the times. You are my girlfriend remember." Chase said confused.
"Huh? But I like boys..." Mary tried to say but had to think to herself. "Is it true? Do I like her like that? Maybe it's why I never was able to confess to him before. Yeah. This is so much better." Mary said to herself before blushing again.
"Mary, you in there?" Chase asked her.
"Why sure cutie. I'm here." Mary said pulling Chase in as she kissed her deeply this time. When she pulled back they both were blushing uncontrollably.
"Your so cute. Wanna stroll on the beach?" Chase asked.
"Sure." Mary said smiling as the two began to walk along the shore's of the sand holding each others hands.
Here's a short story about a girl who wishes this boy she likes was the ideal person for her but gets a surprise afterwards...
Pic found here [link]
Pic found here [link]


text:
Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).
Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53.
We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.
Some DNA microarray studies may target discovery of individual genes whose expressions are associated with a phenotype. Useful statistical approaches have been proposed for such individual-gene analyses, for example, Significance Analysis of Microarray (SAM) in [1]. In many instances, however, the goal of studies is in the assessment of biologic pathways, or a priori defined gene sets, in association with a phenotype, i.e., gene-set analyses. Computationally, gene-set analyses require an additional consideration over individual-gene analyses, namely, the incorporation of gene sets into an association measure. Mootha et al. [2] proposed Gene Set Enrichment Analysis (GSEA) for gene-set analysis, utilizing the Kolmogorov-Smirnov statistic to measure the degree of differential gene expression in a gene set across binary phenotypes. GSEA was revised in 2005 by the same research team, replacing the Kolmogorov-Smirnov statistic with its weighted version to avoid certain deficiencies in the original GSEA method [3]. Many methods have been proposed since for gene-set analyses: see a recent excellent review by Goeman and Bühlmann [4] and their criticisms on the majority of the existing methods. Among the work not covered by the review by Goeman and Bühlmann, Tian et al. [5], in particular, made an important contribution by distinguishing different types of hypotheses in gene-set analysis, and proposed a permutation-based inference using the sum of t-statistic across genes in the gene set as a test statistic. In spite of the large number of gene-set analysis methods, however, GSEA remains by far the most widely used gene-set analysis method to date.
We propose here an alternative, an extension of SAM, to gene-set analysis, called hereafter SAM-GS. This is motivated by our observation that GSEA, in both the original and revised versions, fails to satisfy certain required properties that a gene-set analysis method should satisfy: for example, a gene-set analysis should not indicate an association for a gene set in which no gene is associated with the phenotype. In this paper, we first illustrate the behavior of GSEA in relation to a few simple, required properties of a gene-set analysis method and compare it with the behavior of SAM-GS, using a mouse-microarray kidney-transplant dataset. We then re-analyze, by SAM-GS, three DNA microarray datasets with which the application of GSEA was illustrated in [3], showing appreciable differences in the analysis results. The differences of the results are discussed from both biologic and statistical points of view, pointing out clear advantages of SAM-GS over GSEA.
Results and discussion
Gene-set simulation experiment
Using a mouse-microarray kidney-transplant dataset, we assessed if GSEA and SAM-GS satisfy the following simple requisite properties for any methods designed to perform a gene-set analysis:
(a) If the gene set S consists of genes whose expressions are consistently not associated with the phenotype D, the method should not indicate that S is associated with D.
(b) If the gene set S consists of a mix of genes with moderate to strong and weak associations of expressions with the phenotype D, such that an appreciable subset of the genes in S are moderately or strongly associated with the phenotype D, the method should indicate that S is associated with D.
(c) The size of the gene set S should not greatly alter the statistical significance in (a) and (b).
We performed two tests using the mouse-microarray kidney-transplant dataset with simulated gene sets. For Test 1 intended to assess property (a), the gene sets were randomly generated from the null hypothesis region: the expression of genes in these null gene sets were not associated with the phenotype. A sensible gene-set analysis method should not identify these gene sets as statistically significant. For Test 2 intended to assess property (b), the gene sets were generated randomly from an alternative hypothesis region: the expressions of 50% of genes in each gene set were associated with the phenotype either moderately or strongly. A sensible gene-set analysis method should identify these gene sets as statistically significant.
Tables 1 and 2 show the percentages of 100 randomly-generated gene sets whose expressions were found to be associated with the phenotype with a p-value ≤ 0.05, by the gene-set analysis method of interest (GSEA or SAM-GS), under each gene-set sampling region considered. In Test 1, null gene sets were generated by randomly sampling genes from genes with |r| <c, where r denotes the Pearson correlation coefficient of the gene expression with the phenotype and c was 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4, or 0.5. The results in Table 1 indicate that GSEA does not satisfy requisite property (a), because it identified many of these null gene sets to be associated with the phenotype with a p-value ≤ 0.05. In Test 2, truly-associated gene sets were generated by randomly sampling half of the gene set's genes from genes with |r| = c and remaining half from genes with |r| <c, where c was 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. The results in Table 2 indicate that GSEA does not satisfy requisite property (b), because it did not identify many of these truly-associated gene sets as statistically significant with a p-value ≤ 0.05. Moreover, GSEA does not satisfy property (c), as the performance of the method varies greatly with gene-set size.
Table 1. Performance of GSEA and SAM-GS on Test 1. Proportions of randomly generated null gene sets that are identified by each method to be associated with the phenotype (p-value ≤ 0.05) in a mouse-microarray study.
Table 2. Performance of GSEA and SAM-GS on Test 2. Proportions of randomly generated non-null gene sets that are identified by each method to be associated with the phenotype (p-value ≤ 0.05) in a mouse-microarray study.
The results of these tests illustrate two situations where GSEA fails. One is where genes in a gene set cluster somewhere other than in the strong-association region (e.g., all individual genes could have no or very weak association with the phenotype) and GSEA identifies the gene set to be statistically significantly associated with the phenotype. Figure 1 illustrates this situation with one of the null gene sets of Test 1, where the gene set's genes clustered in the region of no or weak association with the phenotype, and yet GSEA p-value of this gene set was < 0.001. In short, GSEA will indicate that gene sets with any clear clustering are statistically significant, regardless of where the clustering occurs. The other situation is where a gene set has a mixture of moderately or strongly associated genes and weakly associated genes. This mixture within a gene set seems biologically plausible: not all genes in a phenotype-associated pathway will show changes in relation to the phenotype. GSEA has very poor power for detecting a differentially-expressed gene set under such mixed situations (Table 2), unless the clustering of some of the gene-set members occur at the moderate-strong association region (e.g., Table 2 with c = 0.9)
Figure 1. A statistically significant GSEA result. An illustration of a statistically-significant GSEA result with 100 genes selected at random from genes with no or weak correlation of expression with the phenotype (|r| < 0.4).
To check whether SAM-GS satisfies the three requisite properties of a gene-set analysis, the same tests were applied as for GSEA, using the same randomly-sampled simulated gene sets. The results of Tests 1 and 2 displayed in Tables 1 and 2 indicate that SAM-GS satisfies properties (a) and (b). Moreover, SAM-GS satisfies property (c), as its performance did not vary with the size of the gene set.
Gene-set analyses of the three datasets with biologically defined gene sets
We compared the performance of the two methods, GSEA and SAM-GS, on the analyses of biologically defined gene sets using three microarray datasets considered in [3]: the sex, p53, and leukemia datasets. The sex dataset consists of mRNA expression profiles from lymphoblastoid cell lines derived from 15 males and 17 females (the phenotype is sex). The p53 dataset consists of expressions for 50 cell lines from the NCI-60 collection of cancer cell lines, for which mutational status of the p53 gene has been reported, with 17 being classified as wild-type, and 33 as carrying mutations in the gene (the phenotype is the mutation status of p53). The leukemia dataset consists of gene expression profiles of cells from 24 acute lymphoid leukemia (ALL) patients and 24 acute myeloid leukemia (AML) patients (the phenotype is ALL vs. AML). The pathways are organized in two catalogs, C1 and C2. The C1 catalog includes gene sets corresponding to human chromosomes and cytogenetic bands, while the C2 catalog includes gene sets that are involved in specific metabolic signaling pathways [3]. The results by GSEA and SAM-GS are summarized in Table 3. In the sex-comparison analysis, the two methods agreed on the associations (FDR ≤ 0.01) with the three Y associated gene sets, the testis-expressed gene set (GSEA FDR = 0.02), and the gene set with genes that escape X inactivation. In addition, SAM-GS established an association with the chrXp22 gene set (SAM-GS FDR ≤ 0.16 vs. GSEA FDR = 1.00). In the p53-comparison analysis, SAM-GS and GSEA agreed on a subset of gene sets with an FDR ≤ 0.01 that included the gene sets of hsp27, p53_UP (GSEA FDR = 0.013), p53 hypoxia, radiation sensitivity (GSEA FDR = 0.07), and p53 (BioCarta). However, SAM-GS identified additional 31 gene sets with an FDR ≤ 0.01, all of which had an FDR ≥ 0.49 for GSEA. These gene sets are shown in Table 4. In the leukemia-comparison dataset, the two methods gave even more discrepant results than the p53-comparison analysis. GSEA identified only five gene sets with an FDR ≤ 0.25 (none with an FDR ≤ 0.01), whereas all of the 182 gene sets were statistically significant (FDR ≤ 0.01) by SAM-GS. Note that the individual-gene analysis showed that 80% of the individual genes in this comparison had an FDR ≤ 0.25, which is in line with the gene-set analysis results of SAM-GS.
Table 3. Results of the analyses of three datasets by GSEA and SAM-GS.
Table 4. The 31 gene sets for which SAM-GS and GSEA strongly disagreed (SAM-GS FDR ≤ 0.01, GSEA FDR ≥ 0.49) in the p53 analysis.
These discrepancies between the two methods are summarized along with the sensitivity and specificity of the GSEA p-value ≤ 0.05 and the area under the receiver operating characteristic curve of GSEA p-value in predicting the SAM-GS p-value ≤ 0.05 (Table 3). Specifically, sensitivity was calculated as the proportion of gene sets with GSEA p-values ≤ 0.05, out of all gene sets with SAM-GS p-values ≤ 0.05. Specificity was calculated as the proportion of gene sets with GSEA p-values > 0.05, out of all gene sets with SAM-GS p-values > 0.05.
Our Tests 1 and 2 suggest that GSEA does not meet some simple requisite criteria for a gene-set analysis method. In particular, Test 1 results suggest that, in a typical microarray experiment involving genes with different degrees of association with the phenotype, GSEA would frequently identify gene sets as statistically significant when all of its genes have observed expressions completely uncorrelated with the phenotype (e.g., Pearson correlation between -0.1 and 0.1). This is not a logical behavior for a gene-set analysis method. Biologically, if a gene set is identified as having expressions that are significantly associated with a phenotype, the gene set should contain at least some genes whose observed expressions are associated with the phenotype. Statistically, the false discovery rate of GSEA for a truly null gene set, tested among truly non-null gene sets, would be appreciably elevated because the observed correlations of the null-gene-set genes with the phenotype would tend to cluster near zero. Although the gene sets in Tests 1 and 2 are randomly-sampled simulated sets, they are not unrealistic gene sets. For example, a Test 1 situation was encountered in the analysis of the sex dataset, where GSEA gave the "cell-cycle arrest genes" a p-value of 0.015 in association with sex (SAM-GS p-value = 0.84). No gene in this gene set has an absolute value of the Pearson correlation of 0.33 or greater, or the SAM p-value < 0.06: this clustering is thus identified incorrectly by GSEA as showing a significant association, failing Test 1. A Test 2 situation was encountered, for example, in the analysis of the leukemia dataset, where GSEA failed to identify the gene set "chr10q24", even though 13 of the 43 genes in the gene set had absolute values of the Pearson correlation of 0.5 or greater (4 genes greater than 0.7) and the chromosomal location of the gene set is biologically relevant given the role of HOX11 in T-cell ALL. The use of GSEA is subject to appreciable false positive and negative findings, as illustrated by the two tests and the results shown in Table 4.
Another critical problem of GSEA is its relative ranking of genes in a gene set in relation to the other genes outside of the gene set. The use of a relative measure in GSEA, rather than an absolute measure, means that important information on the degree of association between each gene and the binary phenotype is discarded. For example, the leukemia dataset had 80% of its 10,056 individual genes with an FDR ≤ 0.25. Regardless of whether such clear differences in gene expression across the binary phenotype are determined by biology, or by more mundane (and biologically irrelevant) differences in sample collection or handling, a gene-set analysis of this dataset should find that many gene sets are associated with the phenotype. GSEA, however, found only five gene sets with an FDR ≤ 0.25 in the leukemia-comparison analysis, inconsistent with the individual-gene analysis results. The cause of the inconsistency is the use of the relative ranking in GSEA. In contrast, SAM-GS found all gene sets in the leukemia dataset to have an FDR ≤ 0.01.
A related, perhaps less serious, issue with GSEA is that, when an individual gene set is of biologic interest, the SAM-GS analysis requires measurement only of the expression of the genes in the gene set to construct the test statistic (except the calculation of s0), whereas GSEA requires measurement of the expression of
- all
Another problematic aspect of GSEA is that its enrichment score considers genes with positive and negative associations with the phenotype separately, even when they have the same degree of associations with the phenotype. Thus, a gene set with a mix of genes with positive and negative associations with the phenotype, although biologically plausible (for instance, due to feedback loops in pathways), is not appropriately evaluated for association with the phenotype by the enrichment score and, therefore, has an improperly low probability of being detected as a phenotype-associated gene set by GSEA.
A gene-set analysis utilizes existing biologic knowledge that maps individual genes into gene sets or pathways. Because of the utilization of existing knowledge in the analysis, a well conducted gene-set analysis can be remarkably powerful. The p53 analysis illustrates this point. Although a very small proportion of individual genes had low p-values in the p53 dataset, SAM-GS indicated larger proportions of gene sets with low p-values. This is because a valid gene-set analysis would take into account a tendency among multiple genes in a gene set. Thus, even if the association of each gene with the phenotype is only moderate, a collection of such genes can be indicated as a phenotype-associated gene set; genes in a gene set need not have the same degree and direction of association with the phenotype for the gene set to be identified as statistically significant by SAM-GS.
In addition to the leukemia-comparison analysis discussed above, which showed an advantage of SAM-GS over GSEA empirically through the consistency of the gene-set analysis results with the individual-gene analysis results, the other two DNA-microarray analyses (sex- and p53-comparison analyses) provided empirical biologic evidence supporting the advantage of SAM-GS over GSEA. Regarding the sex-comparison analysis, Subramanian et al. [3] specifically argue that they would not expect to find enrichment for bands on the X chromosome because most X-linked genes are randomly silenced in females and, therefore, are unlikely to show a male-female (gene-dose) difference. This argument has general merit; however, in the specific case of the chrXp22 gene set, it does not hold because, on the distal portion of the short arm of X, there is a cluster of genes that escape X-inactivation. Indeed, the top five genes of the chrXp22 gene set escape inactivation: two of the five are members of the X-inactivation-escape gene set whose FDR was ≤ 0.01 by both methods; and the other three have been reported to escape X-inactivation [6-8].
The differences in the results of the p53-comparison analysis illuminate biologically relevant performance differences between the two methods. It is appropriate to ask whether the 31 additional pathways identified by SAM-GS over GSEA are plausibly associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways [9], 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling [10]. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53. In the Ck1 pathway, cdk5 phosphorylates p53 so the presence vs. absence of p53 is likely to modify profoundly the effectiveness of this pathway [11]. Ets1 (ets pathway) has been shown to be essential, in mouse embryonic stem cells, to maintain the ability to undergo UV-induced, p53-dependent apoptosis. Ets1, more broadly, may be necessary for p53-dependent gene transactivation [12]. Akt and p53 are, respectively, essential to the transduction of anti-apoptotic and pro-apoptotic pathways. There is an integrated negative feedback loop whereby p53-dependent down regulation of Akt promotes cell death but cell survival signals will recruit Akt, leading to activation of Mdm2 and the inhibition of p53-dependent apoptosis [13]. This may account, in part, for the association between the presence vs. absence of p53 and differences in the SA-TRKA receptor pathway. Proline oxidase is induced by p53 and mediates apoptosis via a calcineurin-dependent pathway [14]. Coproporphyrinogen oxidase (CPO) is a key compound of the MAP 00860 porphyrin/chlorophyll metabolism gene set. It catalyzes a rate-limiting step in heme biosynthesis and may contribute to mitochondrial redox balance. It has recently been shown to be regulated by p53 [15]. Finally, the Wnt and p53 pathways have also been shown to be linked via pro-apoptotic Dkk1, a wnt antagonist [16].
Regarding the form of SAMGS test statistic, is simply the L2-norm of the t-like-statistic vector d = (d1, d2, ⋯, d|s|), the length of the line segment joining the two phenotypes' mean gene-expression vectors of a gene set S. Our null hypothesis is that the mean vectors of expressions of genes in a gene set S do not differ by the phenotype of interest (i.e., this line-segment length is zero), a two-sample multivariate mean test in statistics. The classical multivariate statistics method for a two-sample mean test, Hotelling's T2, addresses this question, but it cannot be applied when |S| > n1 + n2 - 2, where n1 and n2 are the sample sizes in the two groups defining the phenotype D. We would like to emphasize that this condition is often unmet in gene-set analyses of DNA microarray data. Dempster [16,17] introduced a test statistic for comparing highly multivariate samples of two groups, an alternative for Hotelling's T2, when the number of multivariate measurements is large, relative to the sample sizes. Using Dempster's test in the context of microarray data, a potential candidate for a test statistic to be used in Step 2 of SAM-GS, would be the weighted Dempster's (WD) statistic:
where in the denominator is the average of (n1 + n2 - 2) statistically-independent quantities that have the same mean and variance as the numerator under the null hypothesis, created by an orthonormal transformation of multivariate gene expressions in the set S. This test statistic seems to have the advantage of taking into account the multivariate structure of the gene expression measurements in a gene set by dividing the numerator, the L2-norm of the mean-vector difference, by its approximate expectation. However, since a permutation-based test is used, the denominator of WD statistic is unnecessary: as Dempster [18] stated, a permutation test based on the numerator only is equivalent to using the quotient. Given the computational simplicity and the use of permutation in SAM-GS, the L2-norm used in SAMGS is preferred over WD.
The L1-norm of d = (d1, d2, ⋯, d|s|), can be considered, similar to Chung and Fraser [19] who proposed the L1-norm as an alternative to Dempster's use of the L2-norm. While one might expect the two norms to give similar performances overall, since the L1-norm would be less sensitive to extreme values than the L2-norm, the L1-norm may be less powerful in detecting a gene-set with a small number of genes being strongly associated with the phenotype. Test 2 simulation above confirmed this point: as the proportion of genes in a gene set that are correlated with the phenotype (|r| ≥ 0.6) becomes smaller than approximately 30%, the two norms performs differently and the L1-norm is less powerful in detecting the gene set being associated with the phenotype (data not shown).
To account for multiple comparisons (statistical testing of many hypotheses) when multiple gene sets are to be tested, SAM-GS takes the same approach as SAM, estimating a q-value, an upper limit for the FDR, for each gene set. The q-value of a gene set can be determined solely from the p-values of all gene sets tested [20]. The collection of p-values of all gene sets contains information, not only on the statistical significance of each gene for its association with the phenotype, but also on the proportion of gene sets that are not associated with the phenotype, the "null gene-set proportion." Note that the null gene-set proportion is determined by biology: the phenotype is either biologically associated or not associated with each gene set. However, the p-value is a function of sample sizes and noise levels in gene-expression measurements as well as the degree of underlying biological associations. Thus, even if a strong biologic association between a gene set and the phenotype exists, because of small sample sizes and/or high measurement noise levels (features of many DNA microarray observations and experiments), the p-value of the gene set can be large. This is another aspect of the p53 analysis discussed above, where many gene sets have low FDR estimates in spite of the fact that the p-values are not correspondingly low: this is due to an estimated small null gene-set proportion which lowers FDR estimates.
In conclusion, GSEA is subject to some serious problems as a method for gene-set analysis, potentially leading to unnecessarily high false-positive and false-negative discovery rates. SAM-GS, based on the SAM t-like statistic, is proposed as an alternative gene-set analysis method that is statistically sound and has advantages, as illustrated in this paper, from both statistical and empirical biologic perspectives.
GSEA for gene-set analyses
A gene-set analysis for an a priori defined set of genes S in a total of N genes, on a DNA microarray is a test of the null hypothesis that the expression pattern of S is not associated with a phenotype of interest, D. To simplify the discussion, we will consider only a phenotype with two categories, {0, 1}: e.g. presence or absence of a disease. As biologists are often interested in testing multiple gene-sets {S1, ..., Sk}, we will also consider a gene-set analysis for multiple gene-sets, following our discussion of an individual gene-set.
The revised version of GSEA by [3], for an individual gene-set, proceeds as follows.
GSEA Steps
1) Compute the Pearson correlation (or another metric) between each of the N genes with a phenotype D, where the correlation or another metric of the ith gene is denoted by ri.
2) Order the N genes by their correlation values from the maximum to the minimum (the ordered list is denoted by L).
3) Compute the Enrichment Score (ES): start with ES = 0; walk down the ranked list L, from the top rank (i = 1) to the last rank (i = N), increasing ES by if the ith gene belongs to the gene set S, and decreasing ES by 1/(N - |S|) otherwise, where|S| is the number of genes in the set S.
4) Take the absolute value of the maximum deviation from zero of the ES values among the N genes as the test statistic for the gene set S.
5) Permute the labels of the phenotype D and repeat steps 1)- 4). Repeat until all (or a large number of) permutations are considered.
6) Statistical significance for the association of S and D is obtained by comparing the observed value of the test statistic from 3) and its permutation distribution from 5).
The initial version of GSEA proposed in Step [2] used 1/|S|, instead of , for increasing the ES for each gene in S. The use of , or more generally , was motivated by the need to reduce the ES values and the statistical significance of sets clustered near the middle of the ranked list (see Figure 1 and Table 1 in [3]). Although the modified version of GSEA was aimed at reducing the statistical significance of sets not exhibiting biologically relevant correlation with the phenotype, serious problems remain with GSEA as demonstrated here. To run GSEA, we used the Desktop application downloaded from [21], and the options specified in [3], that is, the Pearson correlation of the gene expressions with the phenotype to rank the genes, and weighted ES.
The proposed method, SAM-GS
The main aim of analyzing an individual gene-set is to distinguish between the two biologic conditions (phenotype) based on multivariate measurements of the expression of genes in the gene set. GSEA tests a null hypothesis that rankings of the genes in a gene set according to an association measure with the phenotype categories (e.g., correlation) are randomly distributed over the rankings of all genes, using Kolmogorov-Smirnov statistic. SAM-GS, on the other hand, tests a null hypothesis that the mean vectors of expressions of genes in a gene set does not differ by the phenotype of interest.
Our proposed SAM-GS method is based on individual t-like statistics from SAM, addressing the small variability problem encountered in microarray data, i.e., reducing the statistical significance associated with genes with very little variation in their expressions. SAM-GS for an individual gene-set can be summarized in a few steps.
SAM-GS Steps
1) For each of the N genes, calculate the statistic d as in SAM for an individual-gene analysis:
where the 'gene-specific scatter' s(i) is a pooled standard deviation over the two groups of the phenotype, and s0 is a small positive constant that adjusts for the small variability encountered in microarray data [1].
2) Compute the SAMGS test statistic corresponding to set S:
3) Permute the labels of the phenotype D and repeat 1) and 2). Repeat until all (or a large number of) permutations are considered.
4) Statistical significance for the association of S and D is obtained by comparing the observed value of the SAMGS statistic from 2) and its permutation distribution from 3).
Note that SAM-GS initially measures the gene-expression difference across the binary phenotype in each gene i of the gene set S using di, where the differences are standardized across the genes for their degrees of scatter with the denominators of di's, {s(i) + s0}. It then summarizes these standardized differences in all the genes in the gene set S by SAMGS. The analysis of multiple gene sets can be accommodated in SAM-GS by estimating false discovery rates (FDRs) from p-values of individual sets using the q-value method of [20].
Gene-set simulation experiment
To illustrate the differences between SAM-GS and GSEA, we compared them on the simple requisite properties (a), (b), and (c), described in Results and Discussion, for any method designed to perform a gene-set analysis. We performed two tests using mouse-microarray data, simulating gene sets.
Test 1: Sample n genes by a simple random sampling as a hypothetical gene set from a group of genes with no or weak association with the phenotype, i.e., genes with |r| <c, where c is 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4, or 0.5. Test the association of this n-gene set with the phenotype. Repeat 100 times to check property (a) for each value of c.
Test 2: Sample n genes by a stratified random sampling as a hypothetical gene set such that half of the genes in the set are moderately or strongly associated with the phenotype with |r| <c, and the other half with |r| <c where c is 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. Test the association of this n-gene set with the phenotype. Repeat 100 times to check property (b) for each value of c.
In Test 1, our simple random sampling from the no-or-weak association region creates gene sets that approximate to the null hypothesis such that their members are consistently not associated with the phenotype (e.g., they have a mixture of genes with correlations between -0.01 and 0.01 when c = 0.01) or are variably weakly associated, or not associated (e.g., correlations between -0.4 and 0.4, including many around zero, when c = 0.4). These gene sets should not be called significantly associated with the phenotype. In Test 2, as half of the genes in the gene set are moderately or strongly associated with the phenotype, these gene sets should be identified as significantly associated with the phenotype.
The two tests were performed based on the data from the mouse-microarray kidney transplant study. In this study, we compared two experimental groups of mouse kidney transplants: fully MHC mismatched allografts and MHC identical isografts. A more detailed description of the study is given in the Appendix. Briefly, in both groups, the kidneys have undergone the same surgical procedure of transplantation, but in addition the allograft develops the histologic lesions of rejection due to the immune response by the host, while the isograft does not develop these lesions due to an identical genetic background. We have studied a full timecourse between days 1 and 42 post transplant; the alloimmune response is fully developed at days 5–7, and the injury response in the isografts also peaks at days 5–6. To simplify the comparison between rejecting allografts and non-rejecting isografts, we have therefore selected the data from days 5 and 7 as the basis of this analysis. A total of 12 samples were analyzed: 3 samples each at day 5 and day 7 in allografts, 4 samples in day 5 isografts, and 2 samples in day 7 isografts. The microarray data were obtained by hybridizing mRNA to Affymetrix MOE 430 2.0 microarrays. These arrays contain 45,099 probe sets whose expression was reduced from the probe level to the gene level of 16,612 unique genes by a method described in the GSEA website, by taking the maximum probe set expression of each gene in each sample. We considered a gene set size n of 10, 30, 50, and 100. We ran the same gene-set simulation experiment using the three datasets (sex, p53, leukemia). Results and relevant discussions are given separately as an additional file [see Additional file 1].
Additional file 1. Gene-set simulation experiment results with the sex, p53, and leukemia datasets. The results of the gene-set simulation experiments using the three datasets are given.
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Gene-set analyses of three datasets with biologically defined gene sets
We compared the performance of the two methods, GSEA and SAM-GS, on the analyses of biologically defined gene sets using three microarray datasets considered in [3]: male vs. female lymphoblastoid cells; p53 wild-type vs. mutant cancer cell lines; and ALL vs. AML leukemia cells. The comparison used GSEA results for the three examples, downloaded from GSEA web-page, [21]. We used the datasets and gene-set subcatalogs C1 and C2 from the above web address to be exactly comparable with the GSEA paper [3]. The same datasets and subcatalogs were used for both GSEA and SAM-GS, only including gene sets with sizes between 5 and 500, following the GSEA paper [3].
We did not analyze the lung adenocarcinoma data of three studies (Boston, Michigan, and Stanford studies) in [3] as such an analysis is methodologically problematic: the Michigan study included only patients with stage I or III lung adenocarcinoma, whereas the Boston and Stanford studies did not restrict the stages; the binary phenotype of interest, death, was defined using censored survival data, where the length of follow-up to ascertain death varied appreciably both by patient and across studies (the median follow-up was 49.9, 29.5, and 17.5 months in the Boston, Michigan, and Stanford studies, respectively), leading to inconsistent ascertainment of the binary phenotype (death) across patients and studies (patients with a longer follow-up had a higher chance of being ascertained to have died); and no adjustment was applied to control for possible differences across the studies in treatment, tumor characteristics, and demographics of the patients.
Gene Set Enrichment Analysis (GSEA)
Significance Analysis of Microarray for Gene Sets (SAM-GS)
Availability and requirements
Project name: Statistical methods for biomarker discovery based on high-dimensional gene/protein expression profiles
Project home page: http://www.ualberta.ca/~yyasui/homepage.html webcite
Operating system(s): Microsoft Windows XP
Programming language: R 2.4.1 and Microsoft Excel 2000
Authors' contributions
ID and YY developed the SAM-GS methodology and designed/conducted the methodological study. TM, GE, KF, GJ, and PH performed the mouse transplant microarray study and introduced the gene-set analysis problem to ID and YY, including the GSEA methodology. JP provided biological interpretations of the analysis results of three real-world datasets, in particular, the p53 related pathways. QL and AA contributed significantly to data analysis, refinement of SAM-GS, and programming. The manuscript was written primarily by ID, YY, and JP, and critically reviewed and revised by all authors. All authors read and approved the final manuscript.
Mouse-microarray kidney transplant study
A histogram of Pearson correlation with the phenotype for 16,612 individual genes in the mouse-microarray kidney-transplant study is given separately as an additional file [see Additional file 2].
Additional file 2. Histogram of Pearson correlation with the phenotype in the mouse-microarray kidney-transplant study. Histogram of Pearson correlation with the phenotype for 16,612 individual genes in the mouse-microarray kidney-transplant study.
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Male CBA/J (CBA) and C57Bl/6 (B6) were obtained from Jackson Laboratory (Bar Harbor, ME). Mice were maintained in the Health Sciences Laboratory Animal Services at the University of Alberta. All maintenance and experiments conformed to approved animal care protocols.
Non-life-supporting renal transplants were performed across full MHC and non-MHC disparities as previously described [22] using wild-type CBA mice as donors and wild-type B6 or wild-type CBA as recipients. Hosts did not receive immunosuppression. Naïve kidneys of the appropriate strain and isografts served as controls. Kidneys were harvested on days 1, 2, 3, 4, 5, 7, 14, 21 and 42 post transplant as previously described [22], snap-frozen in liquid nitrogen and stored at -70°C until further analysis. CBA allografts rejecting in wild type hosts (B6) at days 1, 2, 3, 4, 5, 7, 14, 21 and 42 were designated allo.CBA D1–D42; isografts (CBA into CBA) named iso.CBA. Our mouse model of renal transplants across MHC disparities (CBA into B6), studied over the timecourse between days 1–42 simulates the development of the lesions of T cell mediated rejection that we observe in human biopsies. The isografts (CBA into CBA) serve as controls that have undergone the same surgical procedure without evoking an alloimmune response.
We performed microarray analysis on normal mouse kidneys (NCBA), in allo.CBA D1–D42, and isografts D1–21. RNA extraction, dsDNA and cRNA synthesis, hybridization to MOE430 2.0 oligonucleotide arrays (GeneChip, Affymetrix®), washing and staining were carried out according to [24] and as previously described[25]. Equal amounts of RNA from 3 mice (20–25 μg each) were pooled for each array. Data was normalized using RMA using GeneSpring™ software (Version 7.2, Silicon Genetics, CA, USA) as described previously [25].
JDP is supported by grant CA074794 – the Seattle Colorectal Family Registry – from the National Cancer Institute. The Halloran Lab. is supported by Genome Canada, Genome Alberta, Roche Molecular Systems, Hoffmann La-Roche Canada, University of Alberta Hospitals Foundation, Alberta Innovation and Science, Roche Organ Transplant Research Foundation, the Canadian Institutes of Health Research, Kidney Foundation of Canada, Roche Germany, Astellas Canada, and the Muttart Foundation. PH and YY are Canada Research Chairs in Transplant Immunology and Biostatistics/Epidemiologic Methods, respectively, supported by the Canada Research Chair Program. ID and YY are supported by the Alberta Heritage Foundation for Medical Research and YY is supported by Canadian Institute of Health Research. We thank the reviewers for their constructive comments that have improved the exposition of this paper substantially.
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Summary: Yeast Model Uncovers Dual Roles of Mitochondria in the Action of Artemisinin Wei Li1 , Weike Mo1 , Dan Shen1 , Libo Sun1 , Juan Wang1 , Shan Lu1 , Jane M. Gitschier2 , Bing Zhou1* 1 The State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, China, 2 Departments of Medicine and Pediatrics and Howard Hughes Medical Institute, University of California, San Francisco, California, United States of America Artemisinins, derived from the wormwood herb Artemisia annua, are the most potent antimalarial drugs currently available. Despite extensive research, the exact mode of action of artemisinins has not been established. Here we use yeast, Saccharamyces cerevisiae, to probe the core working mechanism of this class of antimalarial agents. We demonstrate that artemisinin's inhibitory effect is mediated by disrupting the normal function of mitochondria through depolarizing their membrane potential. Moreover, in a genetic study, we identify the electron transport chain as an important player in artemisinin's action: Deletion of NDE1 or NDI1, which encode mitochondrial NADH dehydrogenases, confers resistance to artemisinin, whereas overexpression of NDE1 or NDI1 dramatically increases sensitivity to artemisinin. Mutations or environmental conditions that affect electron transport also alter host's
Source: Arnold, Jonathan - Nanoscale Science and Engineering Center & Department of Genetics, University of Georgia
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Retired Air Force Sgt. Glen Tye walked with his grandson and hundreds of others Monday around the Christa McAuliffe Middle School in Jackson in an effort that raised thousands of dollars for veterans.
Between 350 and 400 people ran or walked in the series of races that circumnavigated the school and raised nearly $3,500 for veterans' organizations, said Jennifer Padavano, a McAuliffe seventh-grade science teacher and one of the organizers of the 14th annual Run for Vets.
Watch the video above to see some of the event. ...