the-psoriasis-treatment

Kleiber's law ( ) is one of those “laws” of nature that is both derived from, and seems to fit quite well with, empirical data. It applies to most animals, including humans. The law is roughly summarized through the equation below, where E = energy expenditure at rest per day, and M = body weight in kilograms. Because of various assumptions made in the original formulation of the law, the values of E do not translate very well to calories as measured today. What is important is the exponent, and what it means in terms of relative increases in weight. Since the exponent in the equation is 3/4, which is lower than 1, the law essentially states that as body weight increases animals become more efficient from an energy expenditure perspective . For example, the energy expenditure at rest of an elephant, per unit of body weight, is significantly lower than that of a mouse. The difference in weight does not have to be as large as that between an elephant and a mouse for a clear differ

It is not very hard to find studies supporting one diet or another. Gardner and colleagues, for example, conducted a study in which the Atkins diet came out on top when compared with the Zone, Ornish, and LEARN diets ( ). In Dansinger and colleagues’ study ( ), on the other hand, following the Atkins diet led to relatively poor results compared with the Ornish, Weight Watchers, and Zone diets. Often the diets compared have different macronutrient ratios, which end up becoming the focus of the comparison. Many consider Sacks and colleagues’ conclusion, based on yet another diet comparison study ( ), to be the most consistent with the body of evidence as a whole: “Reduced-calorie diets result in clinically meaningful weight loss regardless of which macronutrients they emphasize”. I think there is a different conclusion that is even more consistent with the body of evidence out there. This conclusion is highlighted by the findings of almost all diet studies where participants were followe

The graph below shows the results of a multivariate nonlinear WarpPLS ( ) analysis including the variables listed below. Each row in the dataset refers to a county in China, from the publicly available China Study II dataset ( ). As always, I thank Dr. Campbell and his collaborators for making the data publicly available. Other analyses based on the same dataset are also available ( ).     - Wheat: wheat flour consumption in g/d.     - Aprot: animal protein consumption in g/d.     - PProt: plant protein consumption in g/d.     - %FatCal: percentage of calories coming from fat.     - Mor35_69: number of deaths per 1,000 people in the 35-69 age range.     - Mor70_79: number of deaths per 1,000 people in the 70-79 age range. Below are the total effects of wheat flour consumption, along with the number of paths used to calculate them, and the respective P values (i.e., probabilities that the effects are due to chance). Total effects are calculated by considering all of the paths connecting

At the time of this writing, there was no Wikipedia article for the term “meatza”, which surprised me a bit given the number of recipes on the web. In fact, I could not find anything concrete about the dish’s tradition or  history. Another thing that surprised me about this dish is how much my family and I like it. It has become a regular weekend treat for us for quite some time now. The recipe below is for a meal that feeds 4-8 people. Like in my previous recipe for a zucchini and onion meatloaf ( ), the ground beef used here has little fat, and thus a relatively low omega-6 content. Most of the fat comes from the ground bison, which has a higher omega-3 to omega-6 ratio. - Prepare some dry seasoning powder by mixing sea salt, parsley flakes, garlic powder, chili powder, and a small amount of cayenne pepper. - Mix 2 lb of very lean ground beef (96/4) with 1 lb of ground bison. - Add the dry seasoning and a whole egg to the ground meat mix. - Vigorously mix by hand until you get a homo

It is not uncommon for those who do weight training to see no gains over long periods of time for certain weight training exercises (e.g., overhead press), even while they experience gains in other types of exercise (e.g., regular squats). HealthCorrelator for Excel (HCE) and its main outputs, coefficients of association and graphs ( ), have been helping some creative users identify the reasons why they see no gains, and break out of the stagnation periods. It may be a good idea to measure the number of seconds of effort per set; in addition to other variables such as numbers of sets and repetitions, and the amount of weight lifted. In some cases, an inverted J curve, full or partial (just the left side of it), shows up suggesting that the number of seconds of effort in a particular type of weight training exercise is a better predictor of muscle gain than the number of repetitions used. The inverted J curve is similar to the one discussed in a previous post on HCE used for weight trai