A protracted battle between a Zika expert at the U.S. Centers for Disease Control and Prevention (CDC) and his superiors over tests for the virus came to light yesterday.
The fracas centers on allegations by CDC’s Robert Lanciotti, chief of the Diagnostics and Reference Laboratory Activity for mosquito-spread viruses in Fort Collins, Colorado. He alleges that the agency’s Emergency Operations Center (EOC) discounted his research in April 2016 and created “a public health threat” by relying on a less dependable human test for the Zika virus
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As first reported by the The Washington Post, Carolyn Lerner, who heads the U.S. Office of Special Counsel, wrote President Barack Obama a detailed letter yesterday about the allegations. She also released several related reports, including an investigation conducted by the Department of Health and Human Services, and Lanciotti’s response. Lerner became involved after Lanciotti was demoted for his actions in May and filed a whistleblower suit. Her office “secured an agreement” with CDC to reinstate him as head of his lab.
At the heart of the dispute is the fact that, in some people who become infected with Zika, viral levels in the blood remain relatively low, making the virus difficult to detect. Lanciotti alleged that a Zika test made by his lab, called Singleplex, was more sensitive than another test he initially helped devise, called Trioplex (which also tested for the related dengue and chikungunya viruses). Both tests rely on the polymerase chain reaction to amplify minute amounts of viral RNA. But Lanciotti said comparison tests in his lab, and a study conducted independently by the Blood Systems Research Institute in San Francisco, California, found that Trioplex failed to detect viral DNA in up to 39% of samples that the Singleplex test had indicated contained Zika.
Officials at EOC, however, had confidence in the Trioplex test. They said it performed equally well as Singleplex in a comparative analysis conducted in a CDC lab in Puerto Rico.
Concerned that state labs would abandon Singleplex for what he saw as the inferior Trioplex, Lanciotti on 21 April emailed 30 state labs, telling them that his lab was continuing to use Singleplex “due to its greater relative sensitivity.” One of the leaders of the EOC lab team told the HHS investigators that Lanciotti’s email "created more trouble and confusion than it clarified."
The HHS investigation team, which did not include CDC employees who worked in EOC or in the zoonotic infectious diseases branch, concluded on 2 September that evidence did not support Lanciotti’s allegations about Singleplex’s superiority, or that there was any specific danger to public health because of the use of Trioplex. The tests conducted in Puerto Rico, the report states, “produced the clearest, most complete, and most reproducible data available to the investigative team, [and] found no difference in sensitivity.” Even if data showing superiority of Singleplex were accurate, the investigators say the 39% “overstates the potential impact” of missed cases; clinicians use the test as only one indicator when making a Zika diagnosis, and if that’s factored in, the missed cases drop to a maximum of 12% even with the most discrepant tests. What’s more, only Trioplex was granted what’s known as “emergency use authorization” by the U.S. Food and Drug Administration.
In addition to a lack of convincing evidence that Trioplex was inferior in comparative analyses, the investigators noted that the test was modified in August to include larger samples of blood or urine, as well as whole blood—all of which should contain higher levels of the Zika virus, making it easier to detect.
In a stinging rebuttal Lanciotti submitted 15 September, he wrote that CDC needed to reevaluate the “entire EOC concept,” which he said relied on “relatively inexperienced individuals making critical decisions.” He stressed the Trioplex test also performed poorly in detection of the four different dengue virus strains in circulation. And he further criticized EOC for promoting “a questionable assay with misleading communications” and for not having “greater transparency.”
Lerner’s letter stressed that “Lanciotti raises serious concerns about each of the CDC's findings, including the methodology for discounting his research,” but she said the “matter is now closed.”
Riding high in their squad car, officers Jamie Pascucci and Joe Kania are cruising the neighborhood of Homewood, scanning the streets for trouble. Pittsburgh, Pennsylvania, has one of the highest murder rates among large U.S. cities, and violent crime is particularly severe in Homewood, a 98% black pocket of aging, pock-marked Victorians on the east side. Young, white officers from outside the neighborhood, Pascucci and Kania patrol using a mixture of police radio, calls to their department's communications center, and instinct. They get occasional help from ShotSpotter, a network of sensors that detects gunshots and relays the information to a laptop mounted between the front seats.
But starting next month, Pascucci and Kania may get a new type of guidance. Homewood is set to become the initial pilot zone for Pittsburgh's "predictive policing" program. Police car laptops will display maps showing locations where crime is likely to occur, based on data-crunching algorithms developed by scientists at Carnegie Mellon University here. In theory, the maps could help cops do a better job of preventing crime.
Many other cities have already adopted similar systems, which incorporate everything from minor crime reports to criminals' Facebook profiles. They're catching on outside the United States as well. Drawing on approaches from fields as diverse as seismology and epidemiology, the algorithms can help bring down crime rates while also reducing bias in policing, their creators say. They replace more basic trendspotting and gut feelings about where crimes will happen and who will commit them with ostensibly objective analysis.
That's a strategy worth trying at a time when relations between U.S. police and minorities are at an all-time low, says Pittsburgh Police Chief Cameron McLay, who acknowledges that policing has a long way to go to fix bias. (Last year, McLay showed up at a New Year's Eve celebration holding a sign that read, "I resolve to end racism @ work.") McLay sees the use of big data—combined with more community-focused strategies—as part of a palliative for policing's ills.
They're not predicting the future. What they're actually predicting is where the next recorded police observations are going to occur.
But civil liberties groups and racial justice organizations are wary. They argue that predictive policing perpetuates racial prejudice in a dangerous new way, by shrouding it in the legitimacy accorded by science. Crime prediction models rely on flawed statistics that reflect the inherent bias in the criminal justice system, they contend—the same type of bias that makes black men more likely to get shot dead by the police than white men. Privacy is another key concern. In Chicago, Illinois, one scientist has helped the police department generate a list of individuals deemed likely to perpetrate or be victims of violent crime in the near future; those people are then told they're considered at risk, even if they have done nothing wrong.
To what degree predictive policing actually prevents crime, meanwhile, is up for debate. Proponents point to quick reductions in crime rates. But John Hollywood, an analyst for RAND Corporation in Arlington, Virginia, who co-authored a report on the issue, says the advantage over other best-practice techniques is "incremental at best."
The notion of crime forecasting dates back to 1931, when sociologist Clifford R. Shaw of the University of Chicago and criminologist Henry D. McKay of Chicago's Institute for Juvenile Research wrote a book exploring the persistence of juvenile crime in specific neighborhoods. Scientists have experimented with using statistical and geospatial analyses to determine crime risk levels ever since. In the 1990s, the National Institute of Justice (NIJ) and others embraced geographic information system tools for mapping crime data, and researchers began using everything from basic regression analysis to cutting-edge mathematical models to forecast when and where the next outbreak might occur. But until recently, the limits of computing power and storage prevented them from using large data sets.
In 2006, researchers at the University of California, Los Angeles (UCLA), and UC Irvine teamed up with the Los Angeles Police Department (LAPD). By then, police departments were catching up in data collection, making crime forecasting "a real possibility rather than just a theoretical novelty," says UCLA anthropologist Jeffrey Brantingham. LAPD was using hot spot maps of past crimes to determine where to send patrols—a strategy the department called "cops on the dot." Brantingham's team believed they could make the maps predictive rather than merely descriptive.
Crimes of the future
One commonly used approach in predictive policing seeks to forecast where and when crime will happen; another focuses on who will commit crime or become a victim.
Diagram: G. Grullón/Science
Postdoctoral scholar George Mohler, now a mathematician at Indiana University-Purdue University, Indianapolis, suggested that borrowing models from seismology might be useful. Earthquakes take place at a relatively fixed rate along existing fault lines, but quakes can also occur in clusters, when an initial quake is followed by aftershocks occurring near in time and space, Brantingham explains. "Crime is actually very similar," he says. Some crimes are caused by built-in features of the environment, like a bar that closes at 2 a.m. every night, unleashing rowdy drunks onto a neighborhood. Others, such as a series of gang murders or a rash of neighborhood burglaries, happen because criminals' success invites more crimes or incites retaliation. Criminologists call this "repeat victimization"—the criminal equivalent of aftershocks.
Brantingham and Mohler developed an algorithm—now a proprietary software package called PredPol—that predicts what will happen within a given police shift. The software, used by 60 police departments around the country, incorporates a narrow set of closely related crime events from both the immediate and longer-term past, with more recent crimes given heavier weight. The software strips personal details and looks at "only what, where, and when," Brantingham says. At the beginning of a shift, officers are shown maps with 150-by-150-meter boxes indicating where crime is likely to flare up. Fighting crime, the company says in promotional slides, is about "getting in the box."
Here in Pittsburgh, Carnegie Mellon data scientists Wil Gorr and Daniel Neill developed a similar program for Chief McLay not long after he arrived in 2014. A fit, genial man who looks like Mr. Clean, McLay previously held what he calls a "retirement job" as head of a national policing association; he decided to get back into active policing just days after Michael Brown, an unarmed black man, was killed in Ferguson, Missouri, triggering nationwide protests. McLay was convinced that improving the use of data in policing would lead to better outcomes.
Like PredPol, Pittsburgh's CrimeScan program has a geographic focus, but it draws on a broader variety of indicators. Gorr and Neill took their inspiration from criminology research showing that criminals tend to be generalists, and they tend to progress from minor to more serious crimes. As a result, the duo hypothesized, reports of minor crimes could help predict potential flare-ups of violent crime. In a gang confrontation, Neill says, "maybe it starts out with harsh words and offensive graffiti, and turns into fist fights, which turn into shootings, which turn into lots of shootings." Along with observations from the recent past, CrimeScan incorporates scores of minor crime offenses and 911 calls—about things like disorderly conduct, narcotics, and loitering—to spit out predictions about city blocks likely to see upsurges in violent crime in the next few days or weeks.
The Chicago police department (CPD), meanwhile, has taken predictive policing one step further—and made it personal. The department is using network analysis to generate a highly controversial Strategic Subject List of people deemed at risk of becoming either victims or perpetrators of violent crimes. Officers and community members then pay visits to people on the list to inform them that they are considered high-risk.
There are some cities where they have done a great job on hot spot policing, and they have terrible relationships with their communities of color.
The Custom Notification program, as it's called, was inspired in part by studies done by Andrew Papachristos, a sociologist at Yale University. Papachristos grew up in Chicago's Rogers Park neighborhood in the 1980s and '90s, at the height of the crack era. Being white insulated him from some of the violence, he says: "The color of my skin meant I never had to join a gang." But one night, Papachristos watched as a gang burned his parents' diner to the ground because they refused to pay extortion money.
Decades later, when he started studying crime, Papachristos wanted to understand the networks behind it. For a 2014 paper, he and Christopher Wildeman of Cornell University studied a high-crime neighborhood on Chicago's West Side. They found that 41% of all gun homicide victims in the community of 82,000 belonged to a network of people who had been arrested together, and who comprised a mere 4% of the population—suggesting, with other studies, that much can be learned about crime by examining the company people keep, Papachristos says.
Intrigued by these ideas, the Chicago police teamed up with Miles Wernick, a medical imaging researcher at the Illinois Institute of Technology in Chicago, to develop the Custom Notification program. Because gang violence was distributed across the city, hot spot policing wasn't as effective in Chicago, says Commander Jonathan Lewin, head of technology for the department. "The geography of the map isn't as helpful as looking at people and how risky a person is," he says.
The list has invited allegations that CPD is veering dangerously close to the flawed "precrime" unit in the sci-fi film Minority Report, which taps the premonitions of a trio of mutated humans to stop potential murderers before they act. And in bringing bad press, the program has contributed to the problems of the beleaguered CPD, which a mayoral task force described last April as having "systemic institutional failures going back decades that can no longer be ignored."
Papachristos—who is not involved with the Strategic Subject List himself—cautions that the program overemphasizes both an individual's potential to offend and the use of policing, rather than other services, to fight crime. That "reinforces the way in which America devalues the lives of young people of color," he wrote in the Chicago Tribune on 1 August.
What's more, the police data that this and other predictive policing programs rely on are skewed toward crimes committed by people of color, says William Isaac, an analyst with the Human Rights Data Analysis Group and a Ph.D. candidate at Michigan State University in East Lansing. That renders any predictions suspect, he says: "They're not predicting the future. What they're actually predicting is where the next recorded police observations are going to occur." Predictions, indeed, can become self-fulfilling prophecies, says Jennifer Lynch of the Electronic Frontier Foundation in San Francisco, California. "We know from past examples that when police are expecting violence, they often respond with violence."
Brantingham, the architect of PredPol, agrees that civil liberties concerns "are really important questions." But he says that predictive policing can be more fair than the status quo: "What's often forgotten is that any time you put an officer in the field there's a risk of civil liberties violations."
Other critics, meanwhile, raise a more fundamental question about predictive policing: Does it even work?
In a 2012 IBM Smarter Planet commercial, a police officer glances at the screen of his squad car, then speeds to a convenience store. He arrives as a clerk is counting money, and moments before a would-be robber shows up. That's science fiction, says RAND's Hollywood—and likely to stay that way. To predict specific crimes, he says, "we would need to improve the precision of our predictions by a factor of 1000."
Crime often clusters in hot spots like those on the map behind Pittsburgh, Pennsylvania, Police Chief Cameron McLay. He hopes that algorithms capable of predicting future hot spots will help make police work less biased.
Stephanie Strasburg
As to whether existing methods of predictive policing work as advertised, by forecasting the likelihood of crime, the evidence is scarce, and the few data points are not encouraging. For instance, an assessment of Chicago's Strategic Subject List program published by Hollywood and fellow RAND researchers last month found that individuals singled out in the pilot phase were no more likely to become victims of homicides than a comparison group. They were, however, more likely to be arrested for a shooting—possibly because, the researchers write, "some officers may have used the list as leads to closing shooting cases." (The program's "scores are not used for probable cause, and individuals cannot be arrested because of a high score," a spokesperson for CPD says.)
Some scholars have tested models' predictive power against historical crime rates, with encouraging results. But evaluating a program once in use can be more complicated. A randomized, controlled study—a design borrowed from medicine—is the gold standard, but few departments are willing to designate a control area or group, where they won't try to predict crime. "The average police chief lasts 3 years," McLay says. "I don't have time for controls." Hollywood adds that with programs like Chicago's, which single out individuals, "no one wants to say, 'I'm not going to perform interventions with the 10 people who are most at risk.'"
The issue is complicated by the fact that algorithms like PredPol's are proprietary, making it difficult for outside scholars or the general public to evaluate their effectiveness. "For the sake of transparency and for policymakers, we need to have some insight into what's going on so that it can be validated by outside groups," Isaac says. But Brantingham says researchers can evaluate the outcome without knowing all the underlying research.
One notable randomized, controlled experiment was conducted by the Shreveport, Louisiana, police department in 2012 with NIJ funding. The study found that the difference in crime reduction between the control and experimental districts was statistically insignificant. But the experiment, which focused on property crimes, also revealed the challenges of such studies. Take-up of predictive hot spot policing among the three experimental districts was high at first, but dropped off after 4 months as enthusiasm waned, likely skewing the results. Commanders in one of the control districts, meanwhile, grew excited by the experimental districts' success at reducing crime and decided to pursue their own targeted operations in known hot spots.
The most extensive independent evaluation of predictive policing so far, the RAND report, is lukewarm about even the most sophisticated predictive methods, stating that "increases in predictive power have tended to show diminishing returns." Hollywood adds that "the places where really sophisticated data mining algorithms shine" are those where "there are very complex nonlinear relationships between input data and output data." (One example is optical character recognition, which is used for digitizing printed texts.) With crime, he adds, "It's much more simple—the more risk, the more crime. There aren't really complicated relationships going on."
Perched at a conference table overlooking the blighted Allegheny-West neighborhood, Chief McLay says he is keenly aware that rolling out CrimeScan will not solve all the Pittsburgh department's problems. "There are some cities where they have done a great job on hot spot policing, and they have terrible relationships with their communities of color," he says.
The key, some experts say, is not to rely only on statistical methods, but to combine them with other approaches. For example, Papachristos is now working with the Chicago Violence Reduction Strategy, a program that identifies individuals at risk of becoming either violent offenders or victims, then gets them access to social services and employment assistance. A few appear on the Strategic Subject List, says Chris Mallette, the program's executive director, but most are selected through old-fashioned observation.
McLay seems to lean toward a similar approach. As CrimeScan launches, he also aims to build relationships with high-crime communities and ensure that big data are used to solve problems rather than simply focus police work. "Therein lies the key: Who finds that sweet spot?" he says. "Who uses just enough data to be really good, and has the relationships that are just robust enough? That's the challenge that policing in this country is facing right now."
In recent years, brain-mapping initiatives have been popping up around the world. They have different goals and areas of expertise, but now researchers will attempt to apply their collective knowledge in a global push to more fully understand the brain.
Thomas Shannon, US Under Secretary of State, announced the launch of the International Brain Initiative on September 19 at a meeting that accompanied the United Nations’ General Assembly in New York City.
Details—including which US agency will spearhead the programme and who will pay for it—are still up in the air. However, researchers held a separate, but concurrent, meeting hosted by the US National Science Foundation at Rockefeller University to discuss which aspects of the programmes already in existence could be aligned under the global initiative. The reaction was a mixture of concerns over the fact that attempting to align projects could siphon money and attention from existing initiatives in other countries, and anticipation over the possibilities for advancing our knowledge about the brain.
“I thought the most exciting moment in my scientific career was when the president announced the BRAIN Initiative in 2013,” says Cori Bargmann, a neuroscientist at the Rockefeller University in New York City and one of the main architects of the US Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. “But this was better.”
A WEALTH OF IDEAS
One of several goals for the intitiative is the creation of universal brain-mapping tools. Promising experimental tools exist, but labs make their own variations in-house and also tend to run experiments in their own ways. This makes it harder for different teams to collaborate or exchange information. At the Rockefeller meeting, physicist Michael Roukes at the California Institute of Technology in Pasadena noted that the industrial revolution only took off once factories with interchangeable components began replacing companies that had one-off machines. “We’re still in the neuroscience craft era,” he says. “Everyone has their secret sauce.”
Another idea proposed at the meeting is the creation of an International Brain Observatory, with tools such as powerful microscopes and supercomputing resources that scientists from around the world could access—similar to the way that astronomers share telescope time. “If you just give people the basic tools, they’ll do better science,” says Alan Evans, a neurologist at McGill University in Montreal, Canada.
Scientists cheered the idea of a virtual, cloud-based data-sharing resource, analogous to the GenBank genomics resource. It can be difficult to align data as each neurology lab has a preferred method of collecting, formatting and analysing their datasets. But Joshua Vogelstein, a neuroscientist at Johns Hopkins University in Baltimore, proposes a virtual International Brain Station that could automatically convert data from human brain scans or animal gene expression into standardized formats that would allow more people to analyse it.
DIFFERENT PRIORITIES
But many attendees worried that marshalling the numerous proposals under one umbrella could backfire. Existing brain-research programmes have different priorities: Japan and China, for instance, are investing heavily in primate research, whereas the United States tends to avoid it for ethical reasons. The European Union’s flagship Human Brain Project (HBP) is focused on understanding the basic science of how the brain works, whereas Canada is mainly interested in creating technologies that can be applied to medicine.
Other concerns expressed at the US-led Rockefeller meeting, intended to marshal support and ideas for the new International Brain Initiative, felt that some attendees were ignoring existing resources. Canada’s nine-year-old CBRAIN programme serves as a clearinghouse for data and methods, and is already used by neuroscientists in 22 countries and the HBP. But Evans says that it is similar to the International Brain Station proposed at the Rockefeller meeting. “It’s like, let’s reinvent the wheel,” he says.
Others worry that the supposedly global initiative would exclude developing countries. “If the only way to do international is for each country to put in $300 million, that will not be international,” says Sandhya Koushika of the Tata Institute of Fundamental Research in Mumbai, India.
Although smaller countries cannot afford to map a marmoset brain, as Japan is doing, Koushika says that they could contribute to resources with patients, model organisms and efforts to design more affordable technologies.
Bargmann says that the point of the Rockefeller meeting was to get a sense of the kinds of programmes already out there, and notes that future meetings will be more focused once they know who will participate.
Overall, scientists are hopeful that this new global initiative will enable them to take brain mapping to the next level. Since several brain research projects have been around for a while, it's easier to compare their strengths and weaknesses and begin to talk pragmatically about what we need to align them, says Christoph Ebell, executive director of the HBP. “I think it is the right moment.”
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Partindo do princípio de individualidades, devemos destacar alguns pontos fundamentais para a elaboração do Plano Alimentar (dieta) realizado por um nutricionista:
- Sexo: É durante a adolescência que as diferenças na composição corporal entre o sexo masculino e o feminino se tornam maiores. O homem passa a produzir maior quantidade do hormônio testosterona, e assim obter um aumento considerável de massa muscular. Esse aumento de massa muscular é responsável pelo aumento do metabolismo basal, ou seja, uma maior necessidade de energia para a manutenção das funções vitais do organismo. No caso das mulheres, ocorre principalmente o aumento do hormônio estrógeno, associado ao aumento da massa gorda (deposito de gordura), principalmente na região do quadril.
- Idade: Aproximadamente a partir dos 30 anos, passamos a ter uma perda de massa muscular, o que representa um gasto de calorias por dia menor, favorecendo o ganho de peso.
- Estilo de vida: O estilo de vida interfere e muito no gasto energético diário. Uma pessoa que passa o dia trabalhando sentada, por exemplo, tem um gasto energético menor do que uma pessoa que passa o dia trabalhando em pé ou andando.
-Pratica esportiva: a necessidade energetica de praticantes de atividade física é maior do que um sedentário, mas mesmo entre praticantes de atividade física as necessidades são muito diferentes, como um simples caminhada ou corrida (onde há variações de velocidade, inclinação e tempo), uma aula de spining, uma aula de step (com o step alto ou baixo), natação, musculação, entre outras práticas. Além disso, a mesma prática esportiva como por exemplo uma aula de step, pode variar de um indivíduo para outro dependendo da intensidade em que o exercício é realizado.
- Composição corporal: Quanto maior a quantidade de musculos, maior será a necessidade energética (metabolismo).
- Hábitos alimentares: No plano alimentar individualizado, alguns hábitos e práticas alimentares, questões culturais e preferências alimentares podem ser levados em consideração e incluídos de forma adequada (quantificada) na dieta.
- Objetivo: As razões para se procurar uma nutricionista ou somente se atentar a alimentação são diversos, entre eles está, a perda de peso, o ganho de massa muscular, o controle do colesterol, o controle da diabetes, a hipertensão, a anemia, a gestação, os motivos estéticos (ex: celulite, pele, etc.), entre outros.
Baseada em uma média (portanto uma grande variância), os produtos industrializados disponibilizam em suas embalagens as rotulagens nutricionais, com percentuais diários de carboidrato, proteína, gordura, entre outros componentes, de uma dieta de 2.000 kcal. Para alguns uma dieta de 2.000 kcal/ dia irá lhe proporcionar ganho de peso, para outros, perda de peso. O que quero dizer com todas estas individualidades, relacionadas a sexo, idade, estilo de vida, prática esportiva, hábitos alimentares , objetivos entre outros fatores, é que cada um de nós tem uma característica, uma rotina, hábitos alimentares, gostos, disponibilidade de tempo e composição corporal diferentes, portanto, temos necessidades energéticas (calorias) diferentes. Daí então, a importância de um plano alimentar individualizado, atendendo as necessidades de cada um de nós.
Siga as indicacoes abaixo: Cálculo do IMC Imc -índice de massa corporal-
Calculadora
Muito complicado? A editora abril criou uma calculadora onde você só precisa adicionar seus dados.
resultado Altura........: Peso..........: Sexo..........: IMC : EXEMPLO: MULHER IMC: 30,06 Resultado de acordo com os critérios da NHANES II survey (National Health and Nutrition Examination Survey), pesquisa realizada nos Estados Unidos entre 1976-1980: Seu IMC ficou em...........................: 30,06 Você é considerada(o) uma pessoa: Acima do peso Para sua altura, seu peso ideal deve ficar entre..: 48,29 e 65,20 kg ( está 10,80 kg acima do peso máximo ideal) Procedimento 30 DIAS SEGUIDO de SUCO DETOX (TOMAR SEM AÇUCAR) - 1 FOLHA DE COUVE - SUCO DE 1 LIMÃO - 1 MAÇA - 1 LARANJA - 1 FATIA DE ABACAXI - GENGIBRE ( 1 PEDAÇO PEQUENO) - 1 LITRO DE AGUA TOMAR DOIS COPOS PELA MANHÃ Objetivo: REEDUCAÇÃO ALIMENTAR - REDUÇÃO DE TAXAS DE : -COLESTEROL LDL - TRIGLICERIDEOS - ACIDO ÚRICO - MELHORIAS HEPÁTICAS - Diminuição da retenção de líquidos - ajuste de pressão arterial - controle do Diabetes - redução de peso e melhoria da qualidade de vida Indicação geral :45 a 60 minutos de caminhada diária Reeducação por tabela de pontos A distribuição do cardápio deve ser feita da seguinte maneira: Café da Manhã- 300 calorias Lanche da Manhã- 100 calorias Almoço- 400 calorias Lanche da Tarde- 200 calorias Jantar- 200 calorias OBJETIVO: conforme a tabela até chegar a 1200 KCAL (335 PONTOS) Exemplo de cardápio CAFÉ DA MANHÃ 1 copo de leite desnatado com café e adoçante 1 fatia de pão integral com requeijão ou margarina light LANCHE DA MANHÃ 1 maçã ou 1 pêra ALMOÇO 3 colheres (sopa rasas) arroz, 1 concha pequena de feijão, 1 filé de frango grelhado, salada de alface, tomate e cenoura ralada LANCHE DA TARDE 1 fruta + 1 barrinha de cereal ou 1 copo de suco de fruta + 2 torradas integrais ou 4 biscoitos água e sal ou 1 xícara de chá + 6 a 8 cookies integrais JANTAR Salada crua variada + 1 filé de peixe assado ou grelhado Sobremesa: 1 porção de gelatina Segunda Opção Duas colheres de strogonofe (colher de arroz), 2 colheres de sopa de batata palha, salada de folhas, pepino e tomate. Evite frituras. Se for ceder e quiser comer alguma fritura, respeite o espaço de quinze dias entre uma ou outra refeição com fritura. Terceira Opção Uma colher de arroz, 1 concha pequena de creme de milho, 1 filé de frango grelhado médio +salada variada de folhas com 2 tomates secos Cardapios alternativos : Dieta Básica Baseada na pirâmide de alimentos: 1200 calorias diárias e balanceadas. CARDÁPIO Segunda-feira Café da manhã 1 xíc. de café ou 1 xíc. de chá com adoçante 1 torradas com 1 fatia de queijo branco 1 fruta Lanche 1 copo (250 ml) de suco de maracujá Almoço 1 prato (sobremesa) de salada de agrião e mini cenouras 1 filé médio (110 g) de peito de frango ao forno 3 col. (sopa) de arroz branco ou 2 ½ integral 1 xíc. (chá) de couve-flor ao vapor 1 pêra Lanche 1 banana-prata (ou ½ nanica) Jantar 1 prato (sobremesa) de salada de palmito e alface americana 2 almôndegas médias (50 g cada) ao sugo 2 col. (sopa) de milho cozido 2 1/2 col. (sopa) de arroz integral 1 xíc. (chá) de salada de frutas Antes de dormir 1 fatia média de queijo-de-minas Terça-feira Café da manhã 1 xíc. (chá) de leite desnatado com 2 col. (sobremesa) de achocolatado 1 fatia de brownie Lanche 1 maçã Almoço 1 prato (sobremesa) de salada de alface crespa e cenoura ralada 1 fatia média de carne cozida 3 col. (sopa) de creme de milho 2 col. (sopa) de arroz 1 laranja) Lanche 1 taça de gelatina diet Jantar 1 prato (sobremesa) de salada de acelga e beterraba cozida 2 conchas (120 g) de estrogonofe de frango 2 col. (sopa) de arroz 2 pêssego Antes de dormir 1 copo de iogurte desnatado Quarta-feira Café da manhã 1 xíc. de café com adoçante ½ pão francês 1 fatia média de queijo-de-minas 1 banana nanica Lanche 1 copo (250 ml) de suco de uva Almoço 1 prato (sobremesa) de salada de acelga com ½ pepino e 1 tomate fatiado 1 filé médio (120 g) de peixe grelhado 1 batata assada com azeite 3 col. (sobremesa) de couve refogada 1 fatia média de melão Lanche 1 copo (250 ml) de leite desnatado com achocolatado Jantar 1 prato (sobremesa) de salada de folhas verdes com berinjela 1 filé médio (120 g) de peito de frango cozido sem pele 3 col. (sopa) de vagem e cenoura refogadas 3 col. (sopa) de arroz branco 1 laranja Antes de dormir 1 taça de gelatina diet Quinta-feira Café da manhã 1 xíc. de café preto puro 1 fatia de pão de fôrma integral com 1 fatia de peito de peru ½ papaia Lanche 1 taça de salada de frutas Almoço 1 prato (sobremesa) de salada de rúcula com tomate cereja Clara de ovo cozida (1 ovo) 3 col. (sopa) de arroz 2 col. (sopa) de feijão Lanche 1 copo (250 ml) de suco de laranja Jantar 1 prato (sobremesa) de salada de tomate com alface americana 1 xíc. (chá) de macarrão cozido com 2/3 de xíc. (chá) de legumes refogados 1 goiaba Antes de dormir 1 xíc. de chá de ervas 1 fatia média de queijo-de-minas Sexta-feira Café da manhã 1 copo de iogurte de frutas 1 colher (sopa) de cereais Lanche 1 xíc. (chá) de café com leite e adoçante Almoço 1 prato (sobremesa) de salada de alface com mussarela de bufala 4 fatias de carpatio 1 col. (sopa) de arroz 3 col. (sopa) de purê de cenoura 1 figo Lanche 1 fatia média de abacaxi Jantar 1 prato (sobremesa) de legumes no vapor 1 filé médio (120 g) de frango grelhado 2 col. (sopa) de arroz ½ papaia Antes de dormir 1 fatia média de melão 1 copo de leite Sábado Café da manhã 1 xíc. de café com adoçante ½ pão francês 1 fatia grossa de queijo-de-minas Lanche 1 cacho de uva pequeno Almoço 1 prato (sobremesa) de salada de legumes 2 conchas (120 g) de nhoque à bolonhesa 1 xíc. (chá) de salada de frutas Lanche 1 copo de iogurte desnatado com mel Jantar 1 prato (sobremesa) de salada à vinagrete 1 posta média (120 g) de peixe assado 3 col. (sopa) de arroz 3 col. (sopa) de espinafre cozido 1 pera Crie seu cardápio Aqui segue a tabela da dieta dos pontos: Aves Almondega de frango ou peru – 1 unidade – 15 pontos Asa de frango – 1 unidade – 18 pontos Coração de galinha – 1 unidade – 3 pontos Coxa de frango sem pele – 1 unidade – 25 pontos Espetinho de frango – 1 unidade – 31 pontos Estrogonofe de frango – 1 colher (sopa) – 15 pontos Filé de frango grelhado – 1 unidade média – 45 pontos Filé de frango à milanesa – 1 unidade média – 180 pontos Filé de frango à parmigiana – 1 unidade média – 200 pontos Frango à passarinho – 1 pedaço – 25 pontos Hambúrguer de frango – 1 unidade – 30 pontos Lingüiça de frango – 1 unidade – 20 pontos Nugget assado – 1 unidade – 15 pontos Salpicão de frango – 1 colher (sopa) – 13 pontos Salsicha de frango, chester ou peru – 1 unidade – 15 pontos Bebidas Água-de-coco – 200 ml – 10 pontos Bebida energética – 1 lata – 34 pontos Caipirinha de pinga de limão com açúcar – 200 ml – 49 pontos Caipirinha de vodka de limão com açúcar – 200 ml – 55 pontos Caldo de cana – 200 ml – 30 pontos Capuccino – 1 xícara (café) – 20 pontos Cerveja – 1 lata – 40 pontos Champanhe – 1 taça – 30 pontos Chope – 1 copo tulipa – 35 pontos Coquetel de frutas – 100 ml – 22 pontos Gatorede – 500 ml – 35 pontos Licor – 80 ml – 76 pontos Martini – 120 ml – 30 pontos Pinga – 40 ml – 30 pontos Refrigerante – 200 ml – 23 pontos Shake diet com leite – 200 ml – 32 pontos Suco de abacaxi com adoçante – 200 ml – 17 pontos Suco de caju com adoçante – 200 ml – 22 pontos Suco de laranja sem açúcar – 200 ml – 30 pontos Suco de limão com adoçante – 200 ml – 0 pontos Suco de manga sem açúcar – 200 ml – 21 pontos Suco de maracujá com adoçante – 200 ml – 7 pontos Suco de melancia ou melão sem açúcar – 200 ml – 11 pontos Suco de soja light – 200 ml – 25 pontos Suco de uva com adoçante – 200 ml – 11 pontos Uísque – 40 ml – 30 pontos Vinho branco ou tinto – 1 taça de 120 ml – 30 pontos Vodca – 40 ml – 30 pontos Bolachas Biscoito amanteigado – 1 unidade média – 12 pontos Biscoito de polvilho – 1 pacote de 50 g – 60 pontos Bolacha água e sal – 1 unidade – 8 pontos Bolacha de chocolate sem recheio – 1 unidade – 8 pontos Bolacha de leite ou coco – 1 unidade – 8 pontos Bolacha recheada – 1 unidade – 18 pontos Cookie – 1 unidade – 15 pontos Carnes Almôndega – 2 unidade – 45 pontos Bife grelhado – 1 unidade média – 55 pontos Bife de picanha – 1 unidade média – 100 pontos Bisteca de porco – 100 g – 90 pontos Bolo de carne recheado com queijo e presunto – 1 pedaço pequeno – 27 pontos Carne de panela – 3 fatias finas – 21 pontos Carne de soja – 150 g – 45 pontos Carne moída – 1 colher (sopa) – 15 pontos Carne-seca – 1 colher (sopa) – 23 pontos Estrogonofe – 1 colher (sopa) – 15 pontos Hambúrguer – 1 unidade – 30 pontos Lingüiça bovina ou suína – 1 unidade – 45 pontos Lombo de porco – 1fatia média – 45 pontos Picadinho de carne – 1 colher (sopa) – 15 pontos Rosbife – 1 fatia fina – 23 pontos Salsicha – 1 unidade – 30 pontos Cereais e farináceos Amido de milho – 1 colher (sopa) – 20 pontos Arroz (branco, á grega ou integral) – 1 colher (sopa) – 10 pontos Aveia – 1 colher (sopa) – 20 pontos Farinha de milho – 1 colher (sopa) – 15 pontos Farinha de rosca – 1 colher (sopa) – 17 pontos Farinha de trigo – 1 colher (sopa) – 20 pontos Farinha láctea – 1 colher (sopa) – 17 pontos Fubá – 1 colher (sopa) – 20 pontos Granola – 1 colher (sopa) – 20 pontos Sucrilhos – 1 colher (sopa) de – 5 pontos Doces Açaí com granola – 1 tigela de 200 g – 140 pontos Achocolatado – 1 colher (sobremesa) – 15 pontos Açúcar – 1 colher (sopa) – 17 pontos Arroz-doce – 1 colher (sopa) – 10 pontos Bala – 1 unidade – 6 pontos Bala diet – 1 unidade – 2 pontos Bananinha com açúcar – 1 unidade – 30 pontos Bolo comum – sem recheio ou cobertura – 1 fatia fina – 60 pontos Bombom – 1 unidade – 50 pontos Brigadeiro – 1 unidade – 14 Chiclete – 1 unidade – 6 pontos Chiclete diet – 1 unidade – 2 pontos Chocolate – 100 g – 170 pontos Doce de abóbora – 1colher (sopa)– 20 pontos Doce de leite – 1 colher (sopa) – 18 Doces caramelados – 1 unidade pequena – 25 pontos Frutas em calda – 1 unidade – 30 Gelatina – 2 colheres (sopa) – 14 Gelatina light – à vontade Geléia diet – 1 colher (sopa) – 5 pontos Goiabada – 1 fatia fina – 20 pontos Leite condensado – 1 colher (sopa) – 18 pontos Mel – 1 colher (sopa) – 13 pontos Musse de chocolate – 1 colher (sopa) – 20 pontos Paçoca – 1 unidade – 35 Pavê – 1 colher (sopa) – 20 Pipoca doce – 1 saco peq. – 39 Pudim – 2 colheres (sopa) – 55 Quindim – 1 unidade pequena – 60 Sorvete de massa – 1 bola – 55 Sorvete light ou diet – 3 colheres (sopa) – 15 Tortas doces – 1 fatia – 110 Frios Blanquet de peru– 1 fatia – 5 Mortadela – 1 fatia – 15 Presunto – 1 fatia – 10 Presunto de chester -1 fatia – 7 Salame – 1 fatia – 5 Salsichão – 1 fatia – 5 Frutas Abacate – 1 colher (sopa) – 10 Abacaxi – 1 fatia – 10 Ameixa – 1 unidade – 5 Banana-nanica – 1 unidade –30 Banana-prata – 1 unidade – 20 Caju – 1 unidade – 5 Caqui – 1 unidade – 30 Figo – 1 unidade – 10 Fruta-do-conde – 1 unidade – 30 Goiaba – 1 unidade – 15 Jabuticaba – 1 pires (chá) – 10 Kiwi – 1 unidade – 10 Laranja – 1 unidade – 10 Maçã – 1 unidade – 15 Mamão papaia – 1 unidade – 30 Manga – 1 unidade – 40 Maracujá – 1 unidade – 10 Melancia – 1 fatia – 15 Melão – 1 fatia – 10 Morango – 8 unidades – 15 pêra – 1 unidade – 15 Pêssego – 1 unidade – 10 Salada de frutas – 1 colher (sopa) – 5 Tangerina (mexerica) – 1 unidade – 15 Uva – 12 unidades – 10 Leguminosas e tubérculos Batata – 1 unidade – 20 Batata chips – 80 g – 118 Batata-doce – 1 unidade – 40 Batata frita – 1 palito – 8 pontos Batata palha – 1 colher (sopa) – 20 Ervilha 1 colher (sopa) – 5 Feijão – 1 colher (sopa) – 5 Feijão-preto – 1 concha – 27 Lentilha – 1 colher (sopa) – 5 Mandioca cozida – 1 pedaço – 20 Mandioca frita – 1 pedaço – 60 Mandioquinha – 1 unidade – 20 Purê de batata – 1 colher (sopa) – 20 Soja – 1 colher (sopa) – 10 Leite e derivados Coalhada fresca – 200 ml – 40 Creme de leite – 1 colher (chá) 10 Danoninho – 1 unidade – 17 Iogurte de morango – 120 ml – 47 Iogurte desnatado – 200 ml – 25 ogurte diet ou light – 1 unidade – 14 Leite de coco – 1 colher (sopa) 15 Leite integral – 200 ml – 35 Leite semidesnatado – 200 ml – 25 Todinho – 1 unidade – 55 Massas Capelete ou ravióli – 1 xícara (chá) – 50 Lasanha à bolonhesa – 1 pedaço grande – 110 Lasanha ao sugo – 1 pedaço grande – 95 asanha aos 4 queijos – 1 pedaço grande – 239 Macarrão alho e óleo – 1 escumadeira – 37 Macarrão ao sugo – 1 escumadeira – 30 Macarrão com molho branco – 1 escumadeira – 47 Macarrão instantâneo com tempero – 1 porção – 80 Nhoque ao sugo – 1 colher (sopa) – 14 Panqueca de carne ou frango ao sugo – 1 unidade – 60 Risoto de frango – 1 colher (sopa) – 20 Rondeli – 1 unidade – 40 Molhos Creme de milho – 1 colher (sopa) – 20 Ketchup – 1 colher (sopa) 6 Maionese – 1 colher (sopa) – 35 Maionese light – 1 colher (sopa) – 18 Molho à bolonhesa – 1 colher (sopa) – 12 Molho branco – 1 colher (sopa) – 20 Molho de iogurte – 1 colher (sopa) – 8 Molho de tomate – à vontade Molho inglês – 1 colher (sopa)- 2 Molho madeira – 1 colher (sopa) 5 Molho rosé – 1 colher (sopa) – 26 Molho tártaro – 1 colher (sopa) – 30 Mostarda – a vontade Vinagrete – à vontade Óleos e gorduras Azeite – 1 colher (sopa) – 20 Bacon – 1 fatia – 20 Manteiga – 1 colher (chá) – 20 Margarina – 1 colher (chá) – 20 Margarina light – 1 colher (chá) – 15 Óleos vegetais – 1 colher (chá) – 20 Ovos Ovo de codorna – 1 unidade – 5 Ovo de galinha – 1 unidade – 21 Pães Baguete – 1 unidade 50g – 40 Bisnaga – 1 unidade – 20 Croissant – 1 unidade – 40 Croissant de frango com catupiry – 1 unidade – 67 Croissant de presunto e queijo – 1 unidade – 68 Pão de batata – 1 unidade – 46 Pão de forma – 1 fatia – 27 Pão de forma light – 1 fatia – 20 Pão de queijo grande – 1 unidade – 89 Pão de queijo pequeno – 1 unidade – 18 Pão doce – 50 g – 50 Pão folhado – 1 unidade – 40 Pão francês – 1 unidade – 40 Pão francês sem miolo– 1 unidade – 30 Pão integral – 1 fatia – 20 Pão italiano – 1 fatia – 40 Pão na chapa com manteiga – 1 unidade – 62 Torrada – 1 unidade – 10 Peixes Atum em óleo – 3 colheres (sopa) – 40 Bacalhau – 1 filé – 40 Badejo – 1 posta média – 20 Camarão – 1 pires de chá – 40 Corvina – 1 filé – 28 Linguado – 1 filé – 20 Merluza – 1 filé – 40 Namorado – 1 filé – 40 Pescada – 1 filé – 40 Salmão – 1 file – 40 Sardinha em óleo – 1 unidade – 20 Petiscos e salgados Acarajé – 1 unidade – 80 amêndoa – 1 unidade – 2 Amendoim – 1 colher (sopa) – 25 Azeitona – 1 unidade – 2 Bolinho de arroz – 1 unidade – 40 Castanha de caju – 1 colher (sopa) 35 Esfiha de carne – 1 unidade – 60 Quibe assado – 1 fatia – 50 Quibe frito – 1 unidade – 90 Nozes – 1 unidade – 10 Pastel de carne – 1 unidade grande – 87 Pastel de palmito – 1 unidade grande – 74 Pastel de queijo – 1 unidade grande – 94 Pipoca – 1 porção de 50 g – 55 Pizza de atum – 1 fatia – 74 Pizza de calabresa – 1 fatia – 100 Pizza de mussarela – 1 fatia – 81 Pizza portuguesa – 1 fatia – 100 Pizza 4 queijos – 1 fatia – 120 Salgadinho assado – 1 unidade pequena – 20 Salgadinho frito – 1 unidade pequena – 30 Queijos Catupiry – 1 colher (sopa) – 20 Cream-cheese – 1 colher (sopa) – 15 Cream-cheese light – 1 colher (sopa) – 10 Gorgonzola – 1 fatia pequena – 20 Mussarela – 1 fatia – 20 Parmesão – 1 colher (chá) – 5 Polenguinho – 1 unidade – 20 Polenguinho light – 1 unidade – 10 Provolone – 1 fatia – 25 Quejo branco – 1 fatia – 15 Quejo prato – 1 fatia – 20 Requeijão – 1 colher (sopa) – 20 Requeijão light – 1 colher (sopa) – 10 Ricota – 1 fatia grande – 25 Sanduíches Bauru – 1 unidade – 110 Beirute sem maionese – 1 unidade – 150 Big Mac – 1 unidade – 164 Cachorro quente – 1 unidade – 92 Cheeseburguer – 1 unidade – 120 Cheeseburguer salada – 1 unidade – 160 Misto-quente – 1 unidade – 100 Queijo-quente – 1 unidade – 100 Sopas Canja – 1 concha – 30 Creme de ervilha – 1 concha – 50 Sopa de feijão – 1 concha – 50 Sopa de legumes com carne/frango – 1 concha – 20 Vegetais Acelga, agrião, aipo, alface, alho-poró, almeirão, aspargo, chicória, couve-de-bruxelas, couve, endívia, erva-doce, escarola, espinafre, jiló, axixe, mostarda, nabo, pepino, rabanete, repolho, rúcula, salsão, tomate à vontade
Calculadora
Muito complicado? A editora abril criou uma calculadoraonde você só precisa adicionar seus dados. Sob indicação médica existe a possibilidade DE utilizar-se de Uso indicativo de Cloreto de Magnésio por 30 dias Dissolver 33g de Cloreto de magnésio em 1 litro de água filtrada Consumir 200 ml a 400 ml por dia O cloreto de magnésio possui os seguintes benefícios: •Funciona como um excelente purificador do sangue, ajudando a equilibrar seu pH. Graças a este benefício, o cloreto de magnésio nos ajuda a prevenir muitas doenças. •Ajuda a eliminar o ácido que se acumula nos rins, promovendo o funcionamento e a saúde renal. •Estimula as funções cerebrais e a transmissão de impulsos nervosos,contribuindo, desta forma, a manter um equilíbrio mental. •É ideal para os esportistas ou pessoas com alto rendimento físico, já que ajuda a prevenir e combater as lesões musculares, cãibras, fadiga e/ou cansaço muscular. •Estimula o bom funcionamento do sistema cardiovascular, prevenindo as doenças do coração. •Ajuda a diminuir os níveis do colesterol ruim, estimulando a boa circulação do sangue e prevenindo doenças. •É um poderoso remédio anti-estresse, que também ajuda a combater a depressão, os enjoos e a fadiga. •É muito importante na regulação da temperatura do corpo. •Previne problemas como as hemorroidas, melhora a saúde intestinal e ajuda em casos como a colite, prisão de ventre, entre outros. •Previne os problemas da próstata e ajuda a combatê-los. •As pesquisas alertaram que pode ajudar a prevenir e a combater tumores cancerígenos. •Fortalece o sistema imunológico, ajudando a prevenir e a combater os resfriados, mucosidades e infecções. •Previne o envelhecimento precoce, já que oferece vitalidade ao corpo e promove a regeneração celular. •É um elemento chave na prevenção da osteoporose, pois atua como um fixador de cálcio nos ossos. •O cloreto de magnésio previne a formação de cálculos renais, impedindo que o oxalato de cálcio se acumule neles. •Promove a saúde da mulher, já que diminui os sintomas da TPM e estimula a regulação hormonal. •Combate os radicais livres, evitando a formação de tumores e verrugas. •Promove a limpeza das artérias, prevenindo ao mesmo tempo a arteriosclerose.
Luciana O. Pereira L.O, Francischi R. P, Lancha Jr. A Obesidade: Hábitos Nutricionais, Sedentarismo e Resistência à Insulina Arq. Bras Endocrinol Metab vol 47 nº 2 Abril 2003.
Viviani M. T., Junior J. R. G, Interações entre os sistemas nervoso e endócrino e tecido adiposo e muscular na regulação do peso corporal durante dietas alimentares. Revista Brasileira de Nutrição Clinica 2006, 21(1):72-7).
Paravino A. B, Portella E. S, Soares E. A - Metabolismo energético em atletas de endurance é diferente entre os sexos Revista de Nutrição v.20 n.3.Campinas maio/jun. 2007.
Scagliusi F. B., Lancha Jr A. H. Estudo do gasto energético por meio da água duplamente marcada: fundamentos, utilização e aplicações Rev. Nutr. Campinas, 2005. jul./ago v.18 n.4
Panza V. P; Coelho M. S .P; Pietro P. F. ; Assis M. Al .A. , Vasconcelos F. A . G. Consumo alimentar de atletas: reflexões sobre recomendações nutricionais, hábitos alimentares e métodos para avaliação do gasto e consumo energéticos Rev.Nutr. vol.20 no.6 Campinas Nov./Dec. 2007
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