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Voting Rights

Sign the petition: Demand that Congress pass the Voting Rights Advancement Act.

SIGN THE PETITION
In 2013, with a right-wing majority the Supreme Court gutted the Voting Rights Act (VRA), the 1965 bill that protected communities of color against racist voter suppression.

Since then Republicans have had a field day eroding voting rights with voter ID laws, gerrymandered districts, huge purges from voter registration rolls, closing polling locations–all of which disproportionately suppress black and brown votes. Trump created a bogus voter fraud commission to advance the lie that rampant voter fraud–repeatedly proven to be nothing more a right-wing myth–threatens our elections.

We sit at a crucial moment in history. Trump is attacking modern civil rights and dragging us closer each day to fascist rule. We cannot put up our best fight if Republicans continue to disenfranchise the people most harmed by Trump’s agenda.

Click to AUTOMATICALLY sign the petition: Demand that Congress pass the Voting Rights Advancement Act.

AUTOMATICALLY sign the petition
Before the 2013 Supreme Court decision to gut the VRA, the DOJ could block changes to election law in states with histories of voter suppression before they went into effect. Now, voter suppression rules can only be challenged after the fact, when people of color have already been blocked from the polls.

The VRAA would establish the strongest voting rights laws ever passed by Congress. Not only would it require states with a recent history of voting discrimination to have any changes to voting laws approved by the DOJ, but it would also give the attorney general the authority to send federal election observers to monitor elections where there’s a risk of voting discrimination.

We need Congress to pass the Voting Rights Advancement Act, but it won’t happen without a massive push from the left. It won’t happen unless we fight hard and don’t give up until Congress gives in to our pressure. We need you to join that fight.

Click to AUTOMATICALLY sign the petition: Demand that Congress pass the Voting Rights Advancement Act.

AUTOMATICALLY sign the petition
Keep fighting,
Irna Landrum, Daily Kos

Posted byconnie dello buonoJanuary 24, 2018Posted inPoliticsLeave a comment on Voting Rights

Low bar set by media for con man

via Low bar set by media for con man

Posted byconnie dello buonoJanuary 24, 2018Posted inMenuLeave a comment on Low bar set by media for con man

Dopamine may have given humans our social edge over other apes

chimps

Male chimpanzees signal their aggression when they display their big canines, in contrast with humans, who show small canines when they smile.

Sergey Uryadnikov/shutterstock.com

Dopamine may have given humans our social edge over other apes

By Ann GibbonsJan. 22, 2018 , 3:10 PM

Humans are the ultimate social animals, with the ability to bond with mates, communicate through language, and make small talk with strangers on a packed bus. (Put chimpanzees in the same situation and most wouldn’t make it off the bus alive.) A new study suggests that the evolution of our unique social intelligence may have initially begun as a simple matter of brain chemistry.

Neuroanatomists have been trying for decades to find major differences between the brains of humans and other primates, aside from the obvious brain size. The human brain must have reorganized its chemistry and wiring as early human ancestors began to walk upright, use tools, and develop more complex social networks 6 million to 2 million years ago—well before the brain began to enlarge 1.8 million years ago, according to a hypothesis proposed in the 1960s by physical anthropologist Ralph Holloway of Columbia University. But neurotransmitters aren’t preserved in ancient skulls, so how to spot those changes?

One way is to search for key differences in neurochemistry between humans and other primates living today. Mary Ann Raghanti, a biological anthropologist at Kent State University in Ohio, and colleagues got tissue samples from brain banks and zoos of 38 individuals from six species who had died of natural causes: humans, tufted capuchins, pig-tailed macaques, olive baboons, gorillas, and chimpanzees. They sliced sections of basal ganglia—clusters of nerve cells and fibers in a region at the base of the brain known as the striatum, which is a sort of clearinghouse that relays signals from different parts of the brain for movement, learning, and social behavior. They stained these slices with chemicals that react to different types of neurotransmitters, including dopamine, serotonin, and neuropeptide Y—which are associated with sensitivity to social cues and cooperative behavior. Then, they analyzed the slices to measure different levels of neurotransmitters that had been released when the primates were alive.

Compared with other primates, both humans and great apes had elevated levels of serotonin and neuropeptide Y, in the basal ganglia. However, in line with another recent study on gene expression, humans had dramatically more dopamine in their striatum than apes, they report today in the Proceedings of the National Academy of Sciences. Humans also had less acetylcholine, a neurochemical linked to dominant and territorial behavior, than gorillas or chimpanzees. The combination “is a key difference that sets apart humans from all other species,” Raghanti says.

Those differences in neurochemistry may have set in motion other evolutionary changes, such as the development of monogamy and language in humans, theorizes Kent State paleoanthropologist Owen Lovejoy, a co-author. He proposes a new “neurochemical hypothesis for the origin of hominids,” in which females mated more with males who were outgoing, but not too aggressive. And males who cooperated well with other males may have been more successful hunters and scavengers. As human ancestors got better at cooperating, they shared the know-how for making tools and eventually developed language—all in a feedback loop fueled by surging levels of dopamine. “Cooperation is addictive,” Raghanti says.

Lovejoy thinks these neurochemical changes were already in place more than 4.4 million years ago, when Ardipithecus ramidus, an early member of the human family, lived in Ethiopia. Compared with chimpanzees, which display large canines when they bare their teeth in aggressive displays, A. ramidus males had reduced canines. That meant that when they smiled—like male humans today—they were likely signaling cooperation, Lovejoy says.

However, it’s a big leap to prove that higher levels of dopamine changed the evolution of human social behavior. The neurochemistry of the brain is so complex, and dopamine is involved in so many functions that it’s hard to know precisely why natural selection favored higher dopamine levels—or even whether it was a side effect of some other adaptation, says evolutionary geneticist Wolfgang Enard at Ludwig Maximilian University of Munich in Germany. But he says this painstaking research to quantify differences in neurochemistry among primates is important, especially as researchers study differences in gene expression in the brain. Raghanti agrees and is now writing a grant to study the brain tissue of bonobos.

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Posted byconnie dello buonoJanuary 24, 2018Posted inMenuTags:apes, bond, dopamine, Emotion, human, serotonin, socialLeave a comment on Dopamine may have given humans our social edge over other apes

Bird poop brings 3.8 million metric tons of nitrogen out of the sea each year

bird
National Geographic Creative/Alamy Stock Photo

Bird poop brings 3.8 million metric tons of nitrogen out of the sea each year

By Sid PerkinsJan. 23, 2018 , 11:00 AM

Long before the rise of modern agriculture, humans relied on three things to bring nitrogen to barren soils: lightning strikes, nitrogen-fixing bacteria, and natural fertilizers. Among those, the nitrogen- and phosphorus-rich guano produced by millions of seabirds (such as nesting cormorants, above) was so prized it went by the name “white gold.” Now, a new study reveals just how rich that “gold” really is. A first-of-its-kind tally suggests that 804 million breeding seabirds and their chicks produce about 591,000 metric tons of nitrogen each year, researchers report today in Nature Communications. Together with guano from nonbreeders, seabirds produce about 3.8 million metric tons of the element annually—a shade higher than what’s transferred to land by all fishing activities, and 75% of the nitrogen fixed by either lightning or bacteria in rice paddies. A separate calculation estimates that nesting birds and chicks also excrete 99,000 metric tons of phosphorus each year. Now here’s the poop: Because about 12% of that nitrogen and 22% of that phosphorous is readily dissolvable, seabird colonies are nutrient “hot spots,” providing rich runoff to land and sea plants living downcurrent, the researchers say. That’s especially true in the waters around Antarctica and its nearby islands, because seabirds there typically are larger and have longer breeding seasons than seabirds elsewhere.

Posted byconnie dello buonoJanuary 24, 2018Posted inMenuTags:bird, nitrogen, poop, seaLeave a comment on Bird poop brings 3.8 million metric tons of nitrogen out of the sea each year

Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study

Brain, awx341, https://doi.org/10.1093/brain/awx341

We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness.

Participant demographics

The sample size-weighted mean age across all epilepsy samples was 34.4 (range: 26.2–40) years, and the weighted mean age of healthy controls was 33.3 (range: 25.2–42.3) years. The weighted mean age at onset of epilepsy and duration of epilepsy were 17.6 (range: 12.1–28.2) years and 17.4 (range: 8.3–28) years, respectively. Females comprised 57% of the total epilepsy sample (range: 34–75% by individual sample), and 53% of the controls (range: 31–71% by individual sample). Case-control differences in age were observed at 8 of 24 research centres, and case-control differences in sex were observed at 2 of 24 research centres (Supplementary Table 5); hence, age and sex were included as covariates in all group comparisons.

Volumetric findings

Compared to controls, the aggregate all-epilepsies group exhibited lower volumes in the left (d = −0.36; P = 1.31 × 10−6) and right thalamus (d = −0.37; P = 7.67 × 10−14), left (d = −0.35; P = 3.04 × 10−7) and right hippocampus (d = −0.34; P = 6.63 × 10−10), and the right pallidum (d = −0.32; P = 8.32 × 10−9). Conversely, the left (d = 0.29; P = 2.14 × 10−12) and right (d = 0.27; P = 3.73 × 10−15) lateral ventricles were enlarged across all epilepsies when compared to controls (Table 2 and Fig. 2A). A supplementary analysis of all-epilepsies, excluding individuals with hippocampal sclerosis or other lesions, revealed similar patterns of volume loss in the right thalamus and pallidum, and bilaterally enlarged ventricles; however, volume differences were not observed in the hippocampus (Supplementary Table 6).

Table 2

Effect size differences between epilepsy cases and healthy controls (Cohen’s d) for the mean volume of subcortical structures, controlling for age, sex and intracranial volume

Structure Phenotype Cohen’s d SE Z score 95% CI P-value I2 N80 Number of controls Number of cases
Amygdala (LH) All-other-epilepsies 0.327 0.065 5.024 0.199–0.455 5.05 x 10−7 45.470 148 1448 998
Amygdala (RH) All-other-epilepsies 0.218 0.057 3.799 0.106–0.33 1.46 x 10−4 31.256 335 1422 989
Hippocampus (LH) MTLE-L −1.728 0.191 −9.056 −2.102 to −1.354 1.35 x 10−19 85.532 7 1412 410
All epilepsies −0.353 0.069 −5.121 −0.488 to −0.217 3.04 x 10−7 71.845 127 1707 2125
Hippocampus (RH) MTLE-R −1.906 0.15 −12.694 −2.2 to −1.611 6.36 x 10−37 72.476 6 1286 336
All epilepsies −0.336 0.054 −6.175 −0.443 to −0.229 6.63 x 10−10 54.801 141 1719 2129
Lateral ventricle (LH) MTLE-L 0.465 0.089 5.203 0.289–0.640 1.96 x 10−7 43.124 74 1417 414
MTLE-R 0.39 0.081 4.808 0.231–0.549 1.52 x 10−6 26.750 105 1291 338
All epilepsies 0.288 0.041 7.025 0.207–0.368 2.14 x 10−12 23.338 191 1722 2135
All-other-epilepsies 0.198 0.045 4.373 0.109–0.287 1.23 x 10−5 0.218 402 1452 996
Lateral ventricle (RH) MTLE-R 0.444 0.065 6.867 0.317−0.57 6.57 x 10−12 0.003 81 1292 338
MTLE-L 0.363 0.093 3.917 0.1814−0.544 8.95 x 10−5 47.227 121 1418 414
All epilepsies 0.268 0.034 7.864 0.2−0.334 3.73 x 10−15 0 220 1722 2137
All-other-epilepsies 0.212 0.046 4.581 0.122−0.303 4.62 x 10−6 3.528 350 1453 996
Pallidum (RH) MTLE-L −0.452 0.09 −5.009 −0.628 to −0.275 5.48 x 10−7 43.985 78 1406 414
MTLE-R −0.451 0.089 −5.071 −0.624 to −0.276 3.96 x 10−7 36.432 79 1278 332
All epilepsies −0.316 0.055 −5.762 −0.424 to −0.208 8.32 x 10−9 55.575 159 1710 2112
All-other-epilepsies −0.235 0.060 −3.942 −0.352 to −0.118 8.07 x 10−5 36.141 286 1440 976
Putamen (LH) MTLE-L −0.385 0.079 −4.878 −0.539 to −0.23 1.07 x 10−6 28.474 107 1352 410
Thalamus (LH) MTLE-L −0.843 0.126 −6.693 −1.089 to −0.595 2.19 x 10−11 70.462 24 1384 408
All epilepsies −0.358 0.074 −4.839 −0.503 to −0.213 1.31 x 10−6 75.649 124 1687 2104
Thalamus (RH) MTLE-R −0.727 0.103 −7.066 −0.928 to −0.525 1.60 x 10−12 51.499 31 1285 335
MTLE-L −0.462 0.117 −3.941 −0.691 to −0.232 8.12 x 10−5 67.376 75 1412 414
IGE −0.403 0.087 −4.633 −0.574 to −0.233 3.60 x 10−6 39.715 98 1210 363
All epilepsies −0.368 0.049 −7.476 −0.464 to −0.271 7.67 x 10−14 44.822 117 1716 2137
All-other-epilepsies −0.305 0.047 −6.502 −0.397 to −0.213 7.92 x 10−11 4.985 170 1446 998

CI = confidence interval; LH = left hemisphere; RH = right hemisphere; SE = standard error; I2 = heterogeneity index; N80 = number of subjects required in each group to yield 80% power to detect significant group differences (P < 0.05, two-tailed). Uncorrected P-values are reported. Subcortical structures that failed to survive Bonferroni correction (P < 1.49 x 10−4) are not reported (see ‘Materials and methods’ section for statistical threshold determination). See Supplementary material for a full list of volume differences with adjustment for false discovery rate (FDR).

Figure 2
Subcortical volume findings. Cohen’s d effect size estimates for case-control differences in subcortical volume, across the (A) all-epilepsies, (B) mesial temporal lobe epilepsies with left hippocampal sclerosis (HS; MTLE-L), (C) mesial temporal lobe epilepsies with right hippocampal sclerosis (MTLE-R), (D) idiopathic generalized epilepsies (IGE), and (E) all-other-epilepsies groups. Cohen’s d effect sizes were extracted using multiple linear regressions, and pooled across research centres using random-effects meta-analysis. Subcortical structures with P-values < 1.49 × 10−4 are shown in heatmap colours; strength of heat map is determined by the size of the Cohen’s d (d < 0 = blue, d > 0 = yellow/red). Image generated using MATLAB, with annotations added using Adobe Photoshop. An interactive version of this figure is available online, via ‘ENIGMA-Viewer’: http://enigma-viewer.org/ENIGMA_epilepsy_subcortical.html. See Supplementary material for guidelines on how to use the interactive visualization.

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Subcortical volume findings. Cohen’s d effect size estimates for case-control differences in subcortical volume, across the (A) all-epilepsies, (B) mesial temporal lobe epilepsies with left hippocampal sclerosis (HS; MTLE-L), (C) mesial temporal lobe epilepsies with right hippocampal sclerosis (MTLE-R), (D) idiopathic generalized epilepsies (IGE), and (E) all-other-epilepsies groups. Cohen’s d effect sizes were extracted using multiple linear regressions, and pooled across research centres using random-effects meta-analysis. Subcortical structures with P-values < 1.49 × 10−4 are shown in heatmap colours; strength of heat map is determined by the size of the Cohen’s d (d < 0 = blue, d > 0 = yellow/red). Image generated using MATLAB, with annotations added using Adobe Photoshop. An interactive version of this figure is available online, via ‘ENIGMA-Viewer’: http://enigma-viewer.org/ENIGMA_epilepsy_subcortical.html. See Supplementary material for guidelines on how to use the interactive visualization.

The MTLE-L subgroup showed lower volumes in the left hippocampus (d = −1.73; P = 1.35 × 10−19), left (d = P = 2.19 × 10−11) and right thalamus (d = −0.46; P = 8.12 × 10−5), left putamen (d = −0.39; P = 1.07 × 10−6), and right pallidum (d = −0.45; P = 5.48 × 10−7). As in the overall group comparison, we observed larger left (d = 0.47; P = 1.96 × 10−7) and right lateral ventricles (d = 0.36; P = 8.95 × 10−5) in MTLE-L patients relative to controls (Table 2 and Fig. 2B).

The MTLE-R subgroup showed lower volumes across a number of regions in the right hemisphere only, including the hippocampus (d = −1.91; P = 6.36 × 10−37), thalamus (d = −0.73; P = 1.6 × 10−12), and pallidum (d = −0.45; P = 3.96 × 10−7), together with increased volumes of the left (d = 0.39; P = 1.52 × 10−6) and right lateral ventricles (d = 0.44; P = 6.57 × 10−12) compared to controls (Table 2 and Fig. 2C).

The IGE subgroup showed lower volumes in the right thalamus (d = −0.4; P = 3.6 × 10−6) compared to controls (Table 2 and Fig. 2D).

The all-other-epilepsies subgroup showed lower volumes in the right thalamus (d = −0.31; P = 7.9 × 10−11) and the right pallidum (d = −0.24; P = 8.1 × 10−5) compared to controls. The all-other-epilepsies subgroup also showed significant enlargements of the left (d = 0.33; P = 5.1 × 10−7) and right amygdala (d = 0.22; P = 1.46 × 10−4), and the left (d = 0.2; P = 1.2 × 10−5) and right lateral ventricles (d = 0.21; P = 4.62 × 10−6) compared to controls (Table 2 and Fig. 2E).

All volume differences can be visualized using the interactive ENIGMA-Viewer tool (Zhang et al., 2017), at http://enigma-viewer.org/ENIGMA_epilepsy_subcortical.html (Supplementary material). Volume differences significant after FDR adjustment can also be visualized at http://enigma-viewer.org/ENIGMA_epilepsy_subcortical_fdr.html (Supplementary Tables 26–30).

Cortical thickness findings

The all-epilepsies group showed reduced thickness of cortical grey matter across seven regions bilaterally, including the left (d = −0.38; P = 1.82 × 10−18) and right precentral gyri (d = −0.4; P = 8.85 × 10−20), left (d = −0.32; P = 2.11 × 10−15) and right caudal middle frontal gyri (d = −0.31; P = 2.09 × 10−9), left (d = −0.31; P = 2.05 × 10−6) and right paracentral gyri (d = −0.32; P = 2.19 × 10−9), left (d = −0.19; P = 1.29 × 10−4) and right pars triangularis (d = −0.2; P = 4.25 × 10−8), left (d = −0.28; P = 1.51 × 10−7) and right superior frontal gyri (d = −0.27; P = 4.49 × 10−6), left (d = −0.19; P = 1.05 × 10−5) and right transverse temporal gyri (d = −0.18; P = 2.81 × 10−5), and left (d = −0.23; P = 9.87 × 10−5) and right supramarginal gyri (d = −0.22; P = 5.24 × 10−5). The all-epilepsies group also showed unilaterally thinner right cuneus (d = −0.2; P = 9.68 × 10−8), right pars opercularis (d = −0.18; P = 6.48 × 10−7), right precuneus (d = −0.28; P = 2.7 × 10−5), and left entorhinal gyrus (d= −0.26; P = 2.04 × 10−5), compared to healthy controls (Table 3 and Fig. 3A). Supplementary analysis in a non-lesional epilepsy subgroup revealed a similar pattern of cortical thickness differences compared to controls, suggesting that the changes observed in our main analysis were not driven by the inclusion of patients with hippocampal sclerosis or other common lesions (Supplementary Table 7).

Table 3

Effect size differences between epilepsy cases and healthy controls (Cohen’s d) for the mean thickness of cortical structures, controlling for age, sex and intracranial volume

Structure Phenotype Cohen’s d SE Z score 95% CI P-value I2 N80 Number of controls Number of cases
Caudal middle frontal gyrus (LH) MTLE-L −0.403 0.07 −5.789 −0.538 to −0.2663 7.07 x 10−9 13.807 98 1344 412
All epilepsies −0.319 0.04 −7.935 −0.397 to −0.24 2.11 x 10−15 17.112 156 1650 2061
All other epilepsies −0.291 0.045 −6.425 −0.38 to −0.202 1.32 x 10−10 0 197 1447 1000
Caudal middle frontal gyrus (RH) MTLE-L −0.441 0.087 −5.089 −0.611 to −0.271 3.61 x 10−7 39.444 82 1348 412
All epilepsies −0.307 0.051 −5.991 −0.407 to −0.206 2.09 x 10−9 46.443 168 1653 2059
All other epilepsies −0.212 0.045 −4.699 −0.301 to −0.124 2.62 x 10−6 0 350 1451 998
Cuneus (RH) All other epilepsies −0.234 0.045 −5.186 −0.323 to −0.146 2.15 x 10−7 0 288 1449 996
All epilepsies −0.204 0.038 −5.333 −0.279 to −0.129 9.68 x10−8 11.423 379 1651 2057
Entorhinal gyrus (LH) MTLE-L −0.445 0.072 −6.158 −0.5865 to −0.303 7.35 x 10−10 0 81 1102 303
All epilepsies −0.264 0.062 −4.261 −0.385 to −0.142 2.04 x 10−5 56.648 227 1402 1724
Fusiform gyrus (LH) MTLE-L −0.359 0.069 −5.183 −0.494 to −0.223 2.19 x 10−7 13.465 123 1339 412
Lateral occipital gyrus (RH) All other epilepsies −0.211 0.045 −4.659 −0.299 to −0.122 3.18 x 10−6 2.50 x 10−3 354 1450 997
Lingual gyrus (RH) All other epilepsies −0.180 0.045 −3.972 −0.268 to −0.091 7.12 x 10−5 1.25 x 10−2 491 1450 996
Paracentral gyrus (LH) MTLE-R −0.505 0.102 −4.944 −0.705 to −0.305 7.67 x 10−7 52.283 63 1292 338
MTLE-L −0.426 0.099 −4.313 −0.62 to −0.232 1.61 x 10−5 53.165 88 1344 412
All epilepsies −0.311 0.065 −4.748 −0.439 to −0.182 2.05 x 10−6 67.476 164 1650 2061
All other epilepsies −0.257 0.045 −5.680 −0.346 to −0.168 1.34 x 10−8 0 239 1447 1000
Paracentral gyrus (RH) MTLE-R −0.421 0.064 −6.538 −0.548 to −0.295 6.24 x 10−11 0.407 90 1296 338
MTLE-L −0.378 0.075 −5.021 −0.526 to −0.231 5.14 x 10−7 23.536 111 1348 412
All other epilepsies −0.351 0.045 −7.733 −0.44 to −0.262 1.05 x 10−14 3.43 x 10−3 129 1451 998
All epilepsies −0.315 0.053 −5.983 −0.418 to −0.212 2.19 x 10−9 49.261 160 1654 2059
Parahippocampal gyrus (LH) MTLE-L −0.3 0.073 −4.11 −0.444 to −0.1572 3.95 x 10−5 19.366 176 1335 410
Pars opercularis (RH) MTLE-R −0.271 0.071 −3.8 −0.411 to −0.131 1.45 x 10−4 12.105 215 1295 338
All epilepsies −0.177 0.036 −4.976 −0.247 to −0.107 6.48 x 10−7 2.624 503 1652 2059
Pars triangularis (LH) All epilepsies −0.192 0.05 −3.828 −0.2897 to −0.094 1.29 x 10−4 44.414 427 1650 2060
Pars triangularis (RH) MTLE-L −0.285 0.06 −4.738 −0.403 to −0.167 2.16 x 10−6 0 195 1346 412
All epilepsies −0.199 0.036 −5.48 −0.27 to −0.128 4.25 x 10−8 4.66 398 1652 2058
All other epilepsies −0.210 0.045 −4.650 −0.299 to −0.122 3.32 x 10−6 2.58 x 10−3 357 1449 998
Precentral gyrus (LH) MTLE-L −0.466 0.081 −5.755 −0.625 to −0.307 8.64 x 10−9 31.602 74 1339 412
MTLE-R −0.415 0.09 −4.596 −0.592 to −0.238 4.31 x 10−6 40.044 93 1287 338
All epilepsies −0.384 0.044 −8.768 −0.469 to −0.298 1.82 x 10−18 27.649 108 1645 2058
All other epilepsies −0.375 0.046 −8.237 −0.464 to −0.286 1.76 x 10−16 5.59 x 10−3 113 1442 997
IGE −0.342 0.071 −4.78 −0.482 to −0.201 1.75 x 10−6 0.003 136 1043 297
Precentral gyrus (RH) MTLE-R −0.52 0.086 −6.073 −0.687 to −0.352 1.25 x 10−9 33.288 60 1293 337
MTLE-L −0.492 0.078 −6.335 −0.6436 to −0.339 2.37 x 10−10 26.33 66 1345 412
All epilepsies −0.399 0.044 −9.102 −0.485 to −0.313 8.85 x 10−20 27.929 100 1649 2054
IGE −0.39 0.072 −5.442 −0.531 to −0.25 5.27 x 10−8 0.005 105 1044 295
All other epilepsies −0.348 0.045 −7.672 −0.437 to −0.259 1.70 x 10−14 0 131 1448 996
Precuneus (LH) MTLE-L −0.536 0.135 −3.965 −0.801 to −0.271 7.35 x 10−5 75.18 56 1343 412
All other epilepsies −0.178 0.047 −3.819 −0.27 to −0.087 1.34 x 10−4 4.474 497 1446 998
Precuneus (RH) MTLE-L −0.473 0.104 −4.558 −0.676 to −0.27 5.16 x 10−6 57.498 72 1348 412
All epilepsies −0.275 0.066 −4.197 −0.404 to −0.147 2.70 x 10−5 67.608 209 1654 2055
All other epilepsies −0.238 0.053 −4.471 −0.343 to −0.134 7.78 x 10−6 22.378 279 1451 994
Superior frontal gyrus (LH) MTLE-L −0.411 0.06 −6.804 −0.529 to −0.292 1.02 x 10−11 0 94 1343 412
All epilepsies −0.283 0.054 −5.251 −0.389 to −0.177 1.51 x 10−7 51.773 197 1649 2059
All other epilepsies −0.243 0.059 −4.138 −0.358 to −0.128 3.51 x 10−5 34.545 267 1446 999
Superior frontal gyrus (RH) MTLE-L −0.365 0.06 −6.051 −0.483 to −0.246 1.44 x 10−9 0 119 1345 412
All epilepsies −0.269 0.059 −4.588 −0.385 to −0.154 4.49 x 10−6 59.483 218 1650 2058
All other epilepsies −0.235 0.052 −4.489 −0.337 to −0.132 7.15 x 10−6 20.049 286 1448 997
Superior parietal gyrus (LH) All other epilepsies −0.224 0.045 −4.954 −0.313 to −0.136 7.27 x 10−7 0.001 314 1444 996
Superior parietal gyrus (RH) All other epilepsies −0.220 0.045 −4.864 −0.309 to −0.131 1.15 x 10−6 0.002 326 1450 997
Supramarginal gyrus (LH) All epilepsies −0.232 0.06 −3.894 −0.348 to −0.115 9.87 x 10−5 59.391 293 1606 1965
Supramarginal gyrus (RH) All epilepsies −0.223 0.055 −4.045 −0.331 to −0.115 5.24 x 10−5 52.895 317 1597 1971
All other epilepsies −0.206 0.047 −4.418 −0.297 to −0.115 9.95 x 10−6 0 371 1395 961
Temporal pole (LH) MTLE-L −0.315 0.068 −4.649 −0.447 to −0.182 3.33 x 10−6 10.901 160 1341 410
Transverse temporal gyrus (LH) MTLE-R −0.312 0.073 −4.249 −0.456 to −0.168 2.15 x 10−5 15.614 163 1289 338
All epilepsies −0.192 0.044 −4.406 −0.278 to −0.107 1.05 x 10−5 28.178 427 1647 2061
Transverse temporal gyrus (RH) All epilepsies −0.182 0.044 −4.188 −0.267 to −0.097 2.81 x 10−5 27.918 475 1654 2059
All other epilepsies −0.18 0.045 −3.982 −0.269 to −0.091 6.84 x 10−5 0.012 486 1451 998

CI = confidence interval; LH = left hemisphere; RH = right hemisphere; SE = standard error; I2 = heterogeneity index; N80 = number of subjects required in each group to yield 80% power to detect significant group differences (P < 0.05, two-tailed). Uncorrected P-values are reported. Cortical regions that failed to survive Bonferroni correction (P < 1.49 x 10−4) are not reported (see ‘Materials and methods’ section for statistical threshold determination). See Supplementary material for a full list of cortical differences with adjustment for false discovery rate (FDR).

Figure 3
Cortical thickness findings. Cohen’s d effect size estimates for case-control differences in cortical thickness, across the (A) all-epilepsies, (B) mesial temporal lobe epilepsies with left hippocampal sclerosis (MTLE-L), (C) mesial temporal lobe epilepsies with right hippocampal sclerosis (MTLE-R), (D) idiopathic generalized epilepsies (IGE), and (E) all-other-epilepsies groups. Cohen’s d effect sizes were extracted using multiple linear regressions, and pooled across research centres using random-effects meta-analysis. Cortical structures with P-values < 1.49 × 10−4 are shown in heatmap colours; strength of heat map is determined by the size of the Cohen’s d (d < 0 = blue, d > 0 = yellow/red). Image generated using MATLAB with annotations added using Adobe Photoshop. An interactive version of this figure is available online, via ‘ENIGMA-Viewer’: http://enigma-viewer.org/ENIGMA_epilepsy_cortical.html. See Supplementary material for guidelines on how to use the interactive visualization. HS = hippocampal sclerosis.

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Cortical thickness findings. Cohen’s d effect size estimates for case-control differences in cortical thickness, across the (A) all-epilepsies, (B) mesial temporal lobe epilepsies with left hippocampal sclerosis (MTLE-L), (C) mesial temporal lobe epilepsies with right hippocampal sclerosis (MTLE-R), (D) idiopathic generalized epilepsies (IGE), and (E) all-other-epilepsies groups. Cohen’s d effect sizes were extracted using multiple linear regressions, and pooled across research centres using random-effects meta-analysis. Cortical structures with P-values < 1.49 × 10−4 are shown in heatmap colours; strength of heat map is determined by the size of the Cohen’s d (d < 0 = blue, d > 0 = yellow/red). Image generated using MATLAB with annotations added using Adobe Photoshop. An interactive version of this figure is available online, via ‘ENIGMA-Viewer’: http://enigma-viewer.org/ENIGMA_epilepsy_cortical.html. See Supplementary material for guidelines on how to use the interactive visualization. HS = hippocampal sclerosis.

The MTLE-L and MTLE-R subgroups showed distinct patterns of cortical thickness reductions when compared to healthy controls (Table 3, Fig. 3B and C). In MTLE-R, lower cortical thickness was reported across four motor regions, including the left (d = −0.51; P = 7.67 × 10−7) and right paracentral gyri (d = −0.42; P = 6.24 × 10−11), and the left (d = −0.42; P = 4.31 × 10−6) and right precentral gyri (d = −0.52; P = 1.25 × 10−9). The MTLE-R subgroup also showed thickness changes in the left transverse temporal gyrus (d = −0.31; P = 2.15 × 10−5), and right pars opercularis (d = −0.27; P = 1.45 × 10−4) (Table 3 and Fig. 3C). By contrast, in MTLE-L, lower thickness was observed across six regions of the motor cortex, including the left (d = −0.43; P = 1.61 × 10−5) and right paracentral gyri (d = −0.38; P = 5.14 × 10−7), left (d = −0.47; P = 8.64 × 10−9) and right precentral gyri (d = −0.49; P = 2.37 × 10−10), and left (d = −0.54; P = 7.35 × 10−5) and right precuneus (d = −0.47; P = 5.16 × 10−6). The MTLE-L group also showed thickness changes across five regions of the frontal cortex, including the left (d= −0.41; P = 1.02 × 10−11) and right superior frontal gyri (d = −0.37; P = 1.44 × 10−9), left (d = −0.4; P = 7.07 × 10−9) and right caudal middle frontal gyri (d = −0.44; P = 3.61 × 10−7), and the right pars triangularis (d= −0.29; P = 2.16 × 10−6). In MTLE-L, thickness alterations were also observed in four regions of the temporal cortex, including the left temporopolar cortex (d = −0.32; P = 3.33 × 10−6), left parahippocampal gyrus (d = −0.3; P = 3.95 × 10−5), left entorhinal gyrus (d = −0.45; P = 7.35 × 10−10), and left fusiform gyrus (d = −0.36; P = 2.19 × 10−7) (Table 3and Fig. 3B).

The IGE subgroup showed reduced thickness in the left (d = −0.34; P = 1.75 × 10−6) and right precentral gyri (d = −0.39; P = 5.27 × 10−8), when compared to healthy controls (Table 3 and Fig. 3D).

The all-other-epilepsies subgroup showed lower thickness across six cortical regions bilaterally, including the left (d = −0.38; P = 1.76 × 10−16) and right precentral gyri (d = −0.35; P = 1.7 × 10−14), left (d = −0.26; P = 1.34 × 10−8) and right paracentral gyri (d = −0.35; P = 1.1 × 10−14), left (d = −0.29; P = 1.32 × 10−10) and right caudal middle frontal gyri (d = −0.21; P = 2.62 × 10−6), left (d = −0.22; P = 7.27 × 10−7) and right superior parietal gyri (d = −0.22; P = 1.15 × 10−6), left (d = −0.24; P = 3.51 × 10−5) and right superior frontal gyri (d = −0.23; P = 7.15 × 10−6), and the left (d = −0.18; P = 1.34 × 10−4) and right precuneus (d = −0.24; P = 7.78 × 10−6) compared to controls. The all-other-epilepsies group also showed unilaterally reduced thickness in six right hemispheric regions, including the cuneus (d = −0.23; P = 2.15 × 10−7), lateral occipital gyrus (d = −0.21; P = 3.18 × 10−6), pars triangularis (d = −0.21; P = 3.32 × 10−6), supramarginal gyrus (d = −0.21; P = 9.95 × 10−6), transverse temporal gyrus (d = −0.18; P = 6.84 × 10−5), and lingual gyrus (d = −0.18; P = 7.12 × 10−5), compared to controls (Table 3 and Fig. 3E).

An interactive 3D visualization of these results is available via the ENIGMA-Viewer tool (Zhang et al., 2017), at http://enigma-viewer.org/ENIGMA_epilepsy_cortical.html (Supplementary material). Cortical thickness differences significant after FDR adjustment can also be visualized at http://enigma-viewer.org/ENIGMA_epilepsy_cortical_fdr.html (Supplementary Tables 31–35).

Duration of illness, age at onset, and age-by-diagnosis effects on brain abnormalities

A secondary analysis identified significant associations between duration of epilepsy and several affected brain regions in the all-epilepsies, MTLE-R, and all-other-epilepsies groups. In the all-epilepsies group, duration of epilepsy negatively associated with volume measures in the left hippocampus (b = −8.32; P = 8.16 × 10−13), left (b = −13.58; P = 3.52 × 10−15), and right thalamus (b = −12.25; P = 1.58 × 10−13), and right pallidum (b = −2.67; P = 1.78 × 10−7), in addition to bilateral thickness measures in the left (b = −0.003; P = 2.99 × 10−11) and right pars triangularis (b = −0.002; P = 4.24 × 10−9), left (b = −0.003; P = 1.61 × 10−15) and right caudal middle frontal gyri (b = −0.003; P = 1.65 × 10−17), left (b = −0.003; P = 1.77 × 10−13) and right supramarginal gyri (b = −0.003; P = 2.58 × 10−19), left (b = −0.003; P = 5.84 × 10− 12) and right precentral gyri (b = −0.003; P = 2.54 × 10−24), left (b = −0.004; P = 1.94 × 10−12) and right superior frontal gyri (b = −0.003; P = 4.65 × 10−11), left (b = −0.004; P = 1.05 × 10−10) and right transverse temporal gyri (b = −0.003; P = 8.24 × 10−10), and left (b = −0.002; P = 5.22 × 10−6) and right paracentral gyri (b = −0.002; P = 5.63 × 10−6). Duration of epilepsy also negatively associated with unilateral thickness measures in the right precuneus (b = −0.003; P = 6.03 × 10−21), right pars opercularis (b = −0.003; P = 5.59 × 10−13), and right cuneus (b = −0.002; P = 1.1 × 10−9; Supplementary Table 8). In the MTLE-R subgroup, duration of epilepsy negatively associated with volume measures in the right hippocampus (b = −22.42; P = 1.1 × 10−7), and the right thalamus (b = −18.11; P = 1.84 × 10−5), and thickness measures in the left transverse temporal gyrus (b = −0.007; P = 8.39 × 10−5; Supplementary Table 8). In the all-other-epilepsies subgroup, duration of epilepsy negatively associated with bilateral thickness measures in the left (b = −0.003; P = 3.39 × 10−7) and right caudal middle frontal gyri (b = −0.003; P = 6.91 × 10−8), left (b = −0.003; P = 1.36 × 10−9) and right superior frontal gyri (b = −0.003; P = 3.16 × 10−7), and the left (b = −0.003; P = 3.17 × 10−5) and right precuneus (b = −0.003; P = 5.01 × 10−9), in addition to unilateral thickness measures in the right precentral gyrus (b = −0.004; P = 1.16 × 10−12), right cuneus (b= −0.003; P = 8.57 × 10−8), right pars triangularis (b = −0.003; P = 5.16 × 10−7), and right supramarginal gyrus (b = −0.003; P = 2.24 × 10−7). Duration of epilepsy also showed a positive association with the size of the left lateral ventricle in the all-other-epilepsies group (b = 13.6; P = 1.17 × 10−5).

In the all-epilepsies group, age at onset of epilepsy negatively associated with thickness measures in the left (b = −0.003; P = 2.66 × 10−15) and right superior frontal gyri (b = −0.003; P = 9.77 × 10−10), left (b = −0.003; P = 2.78 × 10−9) and right pars triangularis (b = −0.003; P = 6.51 × 10−7), right pars opercularis (b = −0.003; P = 5.4 × 10−14), left transverse temporal gyrus (b = −0.003; P = 1.03 × 10−8), and right cuneus (b = −0.001; P = 4.9 × 10−6). In the all-other-epilepsies subgroup, age at onset negatively correlated with thickness measures in the left (b = −0.003; P = 3.21 × 10−8) and right superior frontal gyri (b= −0.002; P = 1.18 × 10−4), left (b = −0.002; P = 8.42 × 10−6) and right precuneus (b = −0.002; P = 7.23 × 10−5), right pars triangularis (b = −0.003; P = 2.53 × 10−5), and right supramarginal gyrus (b = −0.002; P= 2.38 × 10−6). Age at onset also positively associated with the size of the right lateral ventricle in the all-other-epilepsies subgroup (b = 57.73; P = 1.62 × 10−7).

Age at onset negatively associated with other regional volumetric and thickness measures in the all-epilepsies, IGE, MTLE-L, MTLE-R, and all-other-epilepsies groups, but these associated areas showed no significant structural differences in the primary case-control analysis (Table 1 and Supplementary Table 8).

There were no interaction effects between age and syndromic diagnosis in the all-epilepsies, MTLE-L, MTLE-R, IGE, or all-other-epilepsies groups.

Power analyses for detection of case-control differences

In our sample of 2149 individuals with epilepsy and 1727 healthy controls, we had 80% power to detect Cohen’s d effect sizes as small as d = 0.091 at the standard alpha level of P < 0.05 (two-tailed), and 80% power to detect Cohen’s d effect sizes as small as d = 0.149 at the study’s stringent Bonferroni-corrected threshold of P < 1.49 × 10−4.

N80, the number of cases and controls required to achieve 80% power to detect group differences using a two-tailed t-test at P < 0.05, ranged from N80 = 6, to detect group effects in the right hippocampus in our MTLE-R group, to N80 = 503, to detect group effects in the right pars opercularis in our ‘all epilepsies’ group (Tables 2 and 3).

Discussion

In the largest coordinated neuroimaging study of epilepsy to date, we identified a series of quantitative imaging signatures—some shared across common epilepsy syndromes, and others characteristic of selected, specific epilepsy syndromes. Our sample of 2149 individuals with epilepsy and 1727 controls provided 80% power to detect differences as small as d = 0.091 (P < 0.05, two-tailed), allowing us to identify subtle, consistent brain abnormalities that are typically undetectable on visual inspection, or overlooked using smaller case-control designs. This international collaboration addresses prior inconsistencies in the field of epilepsy neuroimaging, providing a robust, in vivo map of structural aberrations, upon which future studies of disease mechanisms may expand.

In the first of five cross-sectional MRI analyses, we investigated a diverse aggregation of epilepsy syndromes, putative causes, and durations of disease. This all-epilepsies group exhibited shared, diffuse brain structural differences across several regions including the thalamus, pallidum, precentral, paracentral, and superior frontal cortices. With the exception of hippocampal volume and entorhinal thickness differences (Supplementary material), these structural alterations were not driven by any specific syndrome or dataset (Supplementary Figs 3 and 7). Our findings suggest a common neuroanatomical signature of epilepsy across a wide spectrum of disease types, complementing recent evidence for shared genetic susceptibility to a wide spectrum of epilepsies (International League Against Epilepsy Consortium on Complex Epilepsies, 2014). Some structural and genetic pathways may be shared across syndromes, despite the heterogeneity of epilepsy and seizure types. This shared MRI signature underpins the contemporary shift towards the study of epilepsies as network phenomena (Caciagli et al., 2014).

In MTLE, as expected, we observed hippocampal volume abnormalities ipsilateral to the patient’s side of seizure onset. Neither MTLE-L nor MTLE-R showed significant contralateral hippocampal volume reductions, confirming that sporadic, unilateral MTLE is not routinely underpinned by bilateral hippocampal damage (Blümcke et al., 2013). Both MTLE groups showed extrahippocampal abnormalities in the ipsilateral thalamus and pallidum, with widespread reductions in cortical thickness, supporting a growing body of literature indicating that MTLE, as an example of a specific disease constellation in the epilepsies, is also a network disease, extending beyond the mesial temporal regions (Keller et al., 2014; de Campos et al., 2016). Disruption of this network, notably in the thalamus (Keller et al., 2015; He et al., 2017) and thalamo-temporal white matter tracts (Keller et al., 2015, 2017), may be associated with postoperative seizure outcome in MTLE.

Patients with left and right MTLE showed distinct patterns of structural abnormalities when compared to controls, resolving conflicting findings from smaller studies, some reporting an equal distribution of structural differences (Liu et al., 2016), and others indicating more diffuse abnormalities, either in left MTLE (Keller et al., 2002, 2012; Bonilha et al., 2007; Kemmotsu et al., 2011; de Campos et al., 2016) or in right MTLE (Pail et al., 2009). The structural differences observed in the present study may reflect a younger age at onset of epilepsy in left MTLE, which occurred, on average, 1.2 years earlier than those with right MTLE (Supplementary Table 20). Independent, large-scale studies of MTLE patients have confirmed a significantly earlier age at onset in left, compared to right, MTLE (Blümcke et al., 2017). Duration-related effects were also observed in right, but not left, MTLE, pointing to possible biological distinctions between the two.

In IGE, a clinically and biologically distinct group of epilepsies typically associated with ‘normal’ MRI on clinical inspection (Woermann et al., 1998), we identified reduced volume of the right thalamus, and thinner precentral gyri in both hemispheres, supporting prior reports of structural (Bernhardt et al., 2009a), electroencephalographic, and functional (Gotman et al., 2005) abnormalities in IGE. These IGE cases were considered typical by reviewing neurologists, suggesting that this common type of epilepsy is also associated with quantifiable structural brain abnormalities.

The precentral gyri, site of the primary motor cortex, showed bilateral structural deficits across all epilepsy groups (all-epilepsies, IGE, MTLE-L, MTLE-R, and all-other-epilepsies), without detectable inter-cohort or between-disease heterogeneity (Supplementary Figs 3–12). Atrophy of the motor cortex has been linked to seizure frequency and duration of epilepsy in MTLE (Coan et al., 2014); here, we observed a negative correlation between precentral (and postcentral) grey matter thickness and duration of epilepsy in the aggregate all-epilepsies group.

The right thalamus also showed evidence of structural compromise across all epilepsy cohorts, re-emphasizing the importance of the thalamus as a major hub in the epilepsy network (He et al., 2017; Jobst and Cascino, 2017). Loss of feed-forward inhibition between the thalamus and its neocortical connections may be epileptogenic (Paz and Huguenard, 2015), and thalamocortical abnormalities have previously been reported in IGE (Gotman et al., 2005; Bernhardt et al., 2009a; O’Muircheartaigh et al., 2012) and MTLE (Mueller et al., 2010; Bernhardt et al., 2012). These findings support prior ‘system epilepsies’ hypotheses of pathophysiology (Avanzini et al., 2012), suggesting that a broad range of common epilepsies share vulnerability within a thalamocortical structural pathway involved in, and likely affected by, seizures (Liu et al., 2003; Bernhardt et al., 2013). Given this study’s cross-sectional design, we cannot determine if these are causative changes, consequences of recurrent seizures, prolonged drug treatment, or a combination of factors. The epilepsies, as a broad group, may involve progressive structural change (Caciagli et al., 2017), indicating the need for large-scale longitudinal studies.

A heterogeneous subgroup of individuals without confirmed diagnoses of IGE or MTLE with hippocampal sclerosis showed similar patterns of structural alterations to those observed in the aggregate all-epilepsies cohort. The findings included enlarged ventricles, smaller right pallidum and right thalamus, and reduced thickness across the motor and frontal cortices.

Hippocampal abnormalities were not observed in this subgroup, suggesting that the patterns of reduced hippocampal grey matter observed in the aggregate group were driven by the inclusion of MTLEs with hippocampal sclerosis. Unlike the IGE, MTLE, and aggregate epilepsy cohorts, this subgroup also showed bilateral enlargement of the amygdala—a phenomenon previously reported in non-lesional localization-related epilepsies (Reyes et al., 2017) and non-lesional MTLEs (Takaya et al., 2012; Coan et al., 2013). Non-lesional MTLEs formed a large proportion of this ‘all-other-epilepsies’ cohort (43.3%; 445 individuals), but the subgroup included many other focal and unclassified syndromes, potentially obscuring specific biological interpretations. Future, sufficiently powered studies will stratify this cohort into finer-grained subtypes to delineate syndrome-specific effects.

Despite its international scale, our study has limitations. All results were derived from cross-sectional data: we cannot distinguish between historical acute damage and progressive abnormalities. We cannot disentangle the relative contributions of environmental and treatment-related factors, including antiepileptic medications, seizure types and frequencies, disease severity, language dominance, and other initial precipitating factors. On average, duration of epilepsy was at least 10 years; longitudinal investigations of new-onset and paediatric epilepsies will provide a more comprehensive understanding. Despite using standardized image processing protocols, quality control, and statistical techniques, some brain measures showed a wide distribution of effect sizes across research centres, which may reflect sample heterogeneity and differences in scanning protocols (Supplementary material).

https://academic.oup.com/brain/advance-article/doi/10.1093/brain/awx341/4818311

Posted byconnie dello buonoJanuary 24, 2018Posted inMenuTags:brain, epilepsy, scan, sizeLeave a comment on Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study

The brain can heal

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Posted byconnie dello buonoJanuary 24, 2018Posted inMenuLeave a comment on The brain can heal

The brain can heal

The brain is neuroplastic. How we eat, sleep and what we ate affects our brain. Our unique genetic code interacts with the environment affecting our brain. Heal your brain to heal your body. Life experiences, stress, trauma and other social factors affect our brain.

The mind influences the body. Food allergies and metabolic imbalances affect our mood and mind. Exercise burns stress chemicals and hormones in our body.  Strengthen your immune system to make your brain stronger.

For seniors, review your medications with your doctors. Motherhealth caregivers provide holistic approach to caregiving.

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1 day ago – Narendra Modi in Davos highlights: The Prime Minister pointed out that the Indian GDP has increased six-fold since the time the last Indian PM visited Davos. … In a historic address at the World Economic Forum’s annual meeting in Davos, Prime Minister Narendra Modi on Tuesday laid …

Davos 2018: Survey of CEO’s by KPMG highlights geopolitics as …

https://www.cnbc.com/…/davos-2018-survey-of-ceos-by-kpmg-highlights-geopolitics&#8230;

2 days ago – Davos sees record attendance as CEOs want answers on geopolitics, KPMG says. … Global auditing firm KPMG has highlighted geopolitical risk as the main issue occupying the minds of attendees at the World Economic Forum (WEF) in Davos, Switzerland. Bill Thomas, chairman of KPMG …

Davos 2018 highlights: Shah Rukh Khan says acid attack victims need …

https://www.hindustantimes.com/…s…/story-SsmPLUYCNPfFQfuJEI4TiI.html

22 hours ago – PM Modi is leading a big government and business delegation to World Economic Forum in Davos, the first Indian prime minister do so in 21 years. … Narendra Modi became the first Indian prime minister to address the World Economic Forum (WEF) at Davos on Tuesday during which he …

WEF Davos 2018 highlights: Narendra Modi warns of three global …

http://www.livemint.com › Politics › Policy

1 day ago – PM Narendra Modi addressed the opening session of the World Economic Forum (WEF 2018) in Davos on Tuesday. Here are the latest updates and developments.

Davos 2017: Highlights From the World Economic Forum – Bloomberg

Video for davos highlights▶ 1:57
https://www.bloomberg.com/…/davos-2017-highlights-from-the-&#8230;

Jan 17, 2017

The best bits from Bloomberg Television’s coverage of the first day of the World Economic Forum in Davos …

Oxfam highlights sharp inequality as Davos elite gathers – ABC News

abcnews.go.com/…/oxfam-highlights-sharp-inequality-davos-elite-gathers-52508457

3 days ago – A CEO from one of the world’s top five global fashion brands has to work for just four days to earn what a garment worker in Bangladesh will earn in an entire lifetime, campaigning group Oxfam International said Monday. In the run-up to the World Economic Forum in the Swiss ski resort of Davos, Oxfam has …

PM Modi in Davos | Highlights: Need to work together to face …

https://economictimes.indiatimes.com › News › Politics and Nation

1 day ago – “Almost all areas of our economy have been opened to foreign direct investment,” said Modi, the first Indian prime minister in two decades to attend the forum.

Davos: World Economic Forum highlights of the week – BBC News

http://www.bbc.com/news/av/…/davos-world-economic-forum-highlights-of-the-week

BBC News looks at some of the highlights of the World Economic Forum (WEF) which was held inDavos this week.

Highlights: What was discussed at Davos 2015? | World Economic Forum

https://www.weforum.org/agenda/2015/…/your-day-by-day-guide-to-davos-highlight&#8230;

Jan 20, 2015 – Davos is a truly global meeting. Not just in terms of the participants we engage and the issues we address but also the attention we attract around the globe. Davos convenes leaders from …

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Posted byconnie dello buonoJanuary 24, 2018Posted inPoliticsTags:2018, DavosLeave a comment on WEF Davos 2018 highlights: Distinction made between ‘good’ and ‘bad’ terrorist

Best Practices in Managed Substance Use Disorder Treatment

Best Practices in Managed Substance Use Disorder Treatment

In the United States, an estimated 22 million people live with a substance use disorder. Despite their prevalence, substance use disorders continue to go untreated: many individuals forgo help for addiction because of the surrounding stigma, while providers often lack the training to properly recognize and diagnose them. Even with a diagnosis, individuals still face fragmented treatment for these chronic conditions and a high rate of relapse. Without the proper supports, recovery can often seem impossible.

At Beacon Health Options (Beacon), recovery from substance use is not only possible—it’s expected. As today’s leading partner for helping people live healthier, more productive lives, Beacon provides superior clinical management for mental health and substance use disorders, a strong employee assistance program, specialty programs for autism and depression, and data- driven analytics to improve the delivery of care.
With more than 30 years of experience in managed care, we understand the unique challenges of addiction. Managing effective, evidence-based programs nationally and across all lines of business, we provide the right tools and resources to identify and treat substance use disorders and help individuals achieve and sustain long-term recovery. Beacon’s proven model of substance use disorder care management relies on seven core features:

1. Development and Management of a High Quality Continuum of Care

We maintain a continuum of care to ensure members receive the right kind of support and setting for the fluctuating intensity of their needs. Our national network spans every state and encompasses all levels of care, from inpatient detoxi cation programs to intensive outpatient rehabilitation and community-based support services, so members can move to a less restrictive setting as soon as their condition improves while remaining in a safe and therapeutic environment. Ultimately, our goal is to transition members into localized settings to access community-based care, support and resources and prevent unnecessary hospitalization.

We maintain strong partnerships with the substance use disorder provider community along the continuum of care. For example, we pay an enhanced rate for Structured Outpatient Addiction Program (SOAP) providers who use Motivational Interviewing or have an af liation with a homeless shelter. Our staff are also often recruited from the provider community—coming from a diverse array of programs and treatment backgrounds, including acute, residential, and outpatient levels of care—and help to ensure positive connections with the substance use disorder provider community across the entire service continuum.

2. Standardization of Screenings and Assessments

Despite their prevalence, substance use disorders frequently go undiagnosed and untreated. This can largely be attributed to individuals’ reluctance to seek help for these conditions due to the surrounding stigma of addiction, as well as a lack of provider training to properly recognize and diagnose these conditions. To help promote earlier engagement and improved outcomes for members, we use empirically validated screening tools to target substance use disorders when they are more manageable. And with more than a third of all mental health care in the U.S. now being performed by primary care doctors, we ensure these tools are readily available to providers in primary and community-based settings.

One of the ways we help medical providers identify individuals who may be at risk for developing a substance use disorder is through Screening, Brief Intervention and Referral to Treatment (SBIRT), a screening tool speci cally designed to target the larger population exhibiting harmful behaviors but not clinically substance-dependent. After undergoing a universal screening, individuals at risk for developing a substance use disorder receive an educational intervention to change their behavior, and, if appropriate, a referral for more extensive assessment or treatment. And because of its standardized provider training and screening guidelines, SBIRT can be administered in nearly every type of health care setting, including primary care, dental of ces, community health centers, and HIV clinics.

3. Endorsement of Uniform Medical Necessity Criteria

To ensure that treatment is effectively managed and that members receive the appropriate level of care, Beacon uses evidence-based medical necessity criteria, which guides decisions around service intensity, treatment setting, need for continuing care, and readiness for discharge. Beacon’s medical necessity criteria for treating substance disorders is based on the ASAM Criteria, a collection of clinical guidelines developed by the American Society of Addiction Medicine (ASAM) and the most widely used set of criteria in the United States for the treatment of substance use issues. The ASAM criteria takes into consideration the comprehensive needs of the member, including strengths, challenges, goals, and life areas. These objective standards establish a spectrum of services which members can move between based on their changing clinical needs, while also identifying the least intensive treatment services a member needs to recover.

The ASAM criteria individualizes treatment times so that members are not limited to a xed number of treatment days. By tailoring our medical necessity criteria to this multi-dimensional approach, Beacon can reunify the system of care, and connect members to the right services at the right time while meeting their unique and comprehensive needs. And to ensure we remain consistent with current clinical best practice, Beacon’s Corporate Executive Medical Management Committee and Company Quality Control review our medical necessity criteria at least annually.

4. Promotion and Adoption of Evidence-Based Services

To produce better outcomes for the treatment of substance use disorders, we promote systematic, evidence-based services developed by established experts in the eld. These programs include:

  • Medication-Assisted Treatment (MAT): As the nation’s opioid crisis continues to grow, Beacon promotes the use of supervised medication in combination with counseling and behavioral therapies to treat the whole person. Beacon’s approach includes real-time support for prescribers, such as an expert staffed support hotline for those treating substance use disorders. By improving access to resources for medication self- management, we can connect more people to the help they need to recover.
  • Structured Outpatient Addiction Program (SOAP): In Massachusetts, we incorporate SOAPs—short-term, structured, clinically intensive group-oriented treatment services— to individuals returning to the community from medically managed detoxification or acute treatment programs, or to individuals needing more intensive treatment than other outpatient programs may provide. We also endorse programs supported by Substance Abuse and Mental Health Services Administration’s (SAMHSA) National Registry of Evidence Based Programs and Practices, including:
  • Matrix Model: Developed through 20 years of experience in real-world treatment settings, the Matrix Model is an intensive, 16-week outpatient treatment approach consisting of relapse-prevention groups, education groups, social-support groups, individual counseling, and urine and breath testing. The program is guided by a therapist, and includes education for loved ones affected by substance use disorders.
  • Motivational Interviewing: We implement focused and goal-oriented Motivational Interviewing (MI) to help people recognize and change their high-risk behavior, and based on the individual’s goals, develop an action plan. Through incorporating MI counseling into their initial intake/orientation session, community-based substance abuse treatment clinics can also improve program retention.
  • Wellness Recovery Action Plan (WRAP): A self-management intervention, WRAP teaches participants how to implement the key concepts of recovery into their everyday lives, and helps them identify their personalized wellness resources in order to make an individualized plan to manage their disorder.

5. Easing the Administrative Burden

To help providers take the focus off of cumbersome administrative tasks and enhance member experience, we offer easy-to-use online assessments, medical necessity determinations, authorizations, and claiming at no cost to the provider. Our streamlined system makes routine tasks such as processing claims, obtaining claims information, and verifying eligibility status easy and convenient. Providers may also receive quick technical assistance by contacting our Help Desk.

We also offer webinars on a quarterly basis for providers and their key staff to learn more information on our various system enhancements, as well as program and administrative updates.

6. Support for Recovery through Peer Services and Long-Term Chronic Care Management

Beacon understands that treating substance use disorders is a continuous process—in fact, the National Institute on Drug Abuse (NIDA) reports that the relapse rate for drug addiction is 40 to 60 percent. Because we recognize the challenges associated with substance relapse, we offer long-term support for substance use disorders based on the principles of recovery and resiliency as we work to return people to their communities.

PEER SERVICES

Beacon’s Peer Services offer members collaborative support through persons who have lived with substance use disorders and reached a significant level of recovery. Our trained Peer Support Specialists offer ongoing assistance and education as members reintegrate back into the community, helping members learn problem-solving skills and other strategies to help them achieve and sustain recovery. Peer Support Specialists can also help members identify and connect with community-based resources to support their unique ongoing needs.

LONG-TERM CHRONIC CARE MANAGEMENT

Our chronic care management addresses the comprehensive needs of members with substance use conditions and helps them transition back into the community by addressing their holistic needs. Our approach promotes the integration of physical and behavioral health services at every key stage of service and improves overall integration and coordination of medical, behavioral, and psychosocial supports. We will work with medical case managers to ensure the development of culturally-specific, individualized care plans that reflect the member’s strengths and self- identified goals. We help members obtain services and connect them with community-based resources while advocating for the member’s needs, desires, and rights.
We endorse a chronic care model that provides an evidence-based framework to increase the quality of care, reduce costs and improve outcomes for individuals with substance use disorders.

This model incorporates the necessary societal, systemic, and legislative overhaul to promote continuous and real improvements in care and clinical outcomes, and has been successfully employed to treat individuals with various common chronic illnesses, such as diabetes.

7. Continuous Outcome Based Program Improvement

We continually measure and improve our substance use disorder programs by using SAMHSA’s National Outcome Measures (NOMs) performance targets that measures real-life progress for members undergoing addiction treatment. Measurements includes targets such as abstinence from drugs, abstinence from alcohol, and member social connectedness. Beacon also uses health risk assessments, surveys, and call/outreach statistics to continually inform and improve the care management program.

Putting Our Best Practices to Work: Beacon’s National Substance Use Disorder Program Experience

KANSAS

Since 2007, we have administered substance use disorder treatment services for non-Medicaid members funded by Kansas’ Substance Abuse Prevention and Treatment (SAPT) block grants. Overseeing integration of both state and federal funds for substance use treatment for adolescents, adults and families, we coordinate a complete and effective network of private and community-based substance use disorder treatment services.
Since 2009, we have also administered the Kansas Driving Under the Influence (DUI) program, a jail diversion program where non-violent offenders who have been convicted of their third or subsequent DUI charge receive medically necessary substance use disorder treatment.

Our substance use disorder program operates on the Recovery-Oriented System of Care (ROSC) approach to substance use disorder treatment and services, supporting a person-centered approach to recovery. Through our ROSC initiative, we improved member outcomes through a variety of services and programs, including:

  • Person-centered case management
  • Peer recovery services, including development of curriculum for potential peer staff
  • Transportation services to help obtain wraparound services
  • Overnight boarding services for women with children
  • Encouraging providers to utilize recovery-oriented services such as Crisis Intervention and Alcoholics Anonymous meetings

To ensure that individuals have immediate access to appropriate treatment, we operate a 24/7 toll-free hotline for immediate support for screening and referral for substance use concerns. We also developed and conducted a pilot project for Medication Assisted Treatment, negotiating a reduced cost for Vivitrol to distribute medications across the state, and recruited and contracted with several labs to provide the required initial and periodic laboratory and biometric testing.

Outcome Data

From 2008 to 2015, we achieved significant results and cost savings:

  • Doubled the number of individuals gaining access to substance use disorder treatment while experiencing annual reductions in funding
  • Decreased overall higher level of care average length of stay by almost 16%:
    • Reintegration: 12% reduction in ALOS
    • Intermediate Adolescent: 25% reduction in ALOS
    • Social Detox: 30% reduction in ALOS
  • Cumulative improvement in NOMS for 2015:
    • Increased members’ social connectedness to their home environment: 1,595%
    • Abstinence from drugs: 998%
    • Abstinence from alcohol: 965%

PENNSYLVANIA

For 15 years, Value Behavioral Health of Pennsylvania, a Beacon Health Options company, has managed substance use disorder services for HealthChoices, Pennsylvania’s county-based Medicaid program. Our substance use and support services make a significant impact in these counties, where low income and rural environments limits access to services for many citizens.

We serve each of our Pennsylvania clients individually, designing responsive, unique programs to meet the specific needs of each county’s staff, members, individuals in recovery, families and providers. Here are some of the services we provide:

  • Our Intensive Care Managers specialize in coordinating care for members with complex substance use conditions, emphasizing care coordination with the member’s Physical Health MCO, with the goal of decreasing consumer hospitalizations and increasing community tenure.
  • Our Peer Specialists and Drug & Alcohol Recovery Specialists provide critical education and problem-solving skills, helping members transition back into the community.
  • We provide training to law enforcement, teachers, and juvenile justice works to recognize the signs and symptoms of substance use disorders, as well as referral protocols.
  • Due to a high and growing rate of opiate and heroin use, we work with counties to evaluate the provider network to support the work of PCPs, and encourage the use of community support programs (i.e., recovery coaching, peer support, housing, and employment support) to ensure a chronic care model is readily available.

Outcome Data

  • Overall 96% satisfaction of consumers and families with services they received
  • In Greene County, the percent of HealthChoices members who have used mental health and/or substance abuse services has increased by more than 61 percent from 2003 through 2012

MARYLAND

As the administrative services organization (ASO) for the Maryland Department of Health and Mental Hygiene/Mental Hygiene Administration, we deliver cost effective, recovery-oriented care for 1.1 million Medicaid and eligible uninsured consumers.

To drive engagement, our community-based staff partners with providers and community support programs where our members live, and we offer PCP training and practice supports through:

  • Screening, Brief Intervention and Referral to Treatment (SBIRT)
  • Promoting Early Detection and Screening of Alcohol Used by Youths
  • Alcohol Prevention and Screening During Pregnancy

To ensure services are accessible, we offer community health works and lay health educators of specific cultural backgrounds to provide a “cultural bridge.” Our eld-based Care Managers are continually in the community to engage individuals face-to-face, and we assist with any transportation barriers (such as providing Metro cards) and provide translation services when needed.

We are committed to a person-centered, recovery-oriented approach that actively involves individuals with lived substance use disorder experience, and employ Peer Specialists as well as contract with local peer-run agencies. Our Peer Specialists have worked with over 1,200 members.

Outcome Data

  • Increased the number of people served while decreasing the average cost per member
  • Aligned cost of care with best practices, resulting in an annualized savings of $4.1 million
  • Consumers reported 94% satisfaction rating with care management staff

MASSACHUSETTS

Since 1996, Massachusetts Behavioral Health Partnership (MBHP), a Beacon Health Options company, has maintained a comprehensive community- based provider network offering a full continuum of acute and post-acute substance use treatment services across the Commonwealth. We currently have contracts with the entire substance use disorder treatment provider community serving the Medicaid population.

Using a Central Navigation System (CNS), we provide information and support for members seeking information about substance use treatment, and help them take full advantage of their substance use bene ts, as well as connect them to community-based services and groups that provide added support. Our team is focused on ensuring individuals receive services in the most appropriate setting to increase engagement in the recovery process. Some of these services include:

  • Intensive Care Coordination
  • Emergency Services Program/Mobile Crisis Intervention (ESP/MCI)
  • Outpatient Substance Use Disorder Services
    • SOAP
    • Ambulatory Detoxi cation
    • Acupuncture Treatment
  • Diversionary Services
    • Acute Treatment Services (ATS) for Substance Use Disorders
    • Enhanced Acute Treatment Services (E-ATS) for Individuals with Co-occurring Mental Health and Substance Use Disorders
    • Clinical Stabilization Services (CSS) for Substance Use Disorders
  • Inpatient Substance Use Disorder Services (Level IV Detoxification Services)

Focus on Opioid Treatment and Prevention

Opioid addiction is an urgent problem in the Commonwealth that involves multiple systemic issues and requires effective long-term solutions in addiction treatment and psychosocial rehabilitation. We are executing a wide range of pilots and programs in Massachusetts to address the rising opioid epidemic. They include:

  • The Changing Pathways project, which helps improve member transitions from inpatient withdrawal management programs to outpatient Medication-Assisted Treatment (MAT).
  • Community Support Programs to promote adherence to MAT, ensure care continuity for members discharged from withdrawal management programs, and improve follow up appointment rates.
  • Intensive Care Management for Methadone Maintenance program where Beacon partners with methadone treatment providers and leverages intensive case management to help improve methadone maintenance adherence. Improved adherence has been shown to reduce inpatient readmissions and mortality for these members.
Posted byconnie dello buonoJanuary 24, 2018Posted inMenuTags:addiction, alcohol, Management, opioids, options, substance abuse, treatmentLeave a comment on Best Practices in Managed Substance Use Disorder Treatment

Low bar set by media for con man

https://youtu.be/pWohqhZefBs

 

Tell the mainstream corporate media: Don’t normalize Trump’s extremism.

Tell the traditional corporate media:
“Donald Trump is a dangerous, racist extremist who wants to divide the country. He has been since he launched his campaign for president. Don’t let the Trump White House fool you with a polished, teleprompter-aided State of the Union speech into normalizing his extremism and the unhinged nature of his entire authoritarian regime.”Add your name:

Sign the petition ►
Dear Connie,

Tell the mainstream corporate media: Don’t normalize Trump’s extremism.

Traditional corporate media outlets have got to do a better job of holding Donald Trump accountable for his unhinged racist extremism.

Their bar for Donald Trump’s performance is literally so low that he recently got credit for remembering the names of people he was meeting with – when their names were written on placards in front of them.1

Trump heads to the Capitol next week to deliver his State of the Union address. We cannot let traditional corporate media outlets, whether on cable networks or in national newspapers, turn their backs on the clear evidence that Trump is a dangerous racist, sexual predator, and warmongering xenophobe who puts our democracy, our country and the whole world at risk. We have to demand they do better.

Tell the mainstream corporate media: One polished State of the Union address doesn’t undo Trump’s fascist extremism. Don’t use the speech to normalize his dangerous agenda. Click here to sign the petition.

Trump’s State of the Union may turn out to be more polished and vetted than his unhinged tweets and racist comments, but it will still promote his dangerous, racist agenda. Unfortunately, unless there is a massive outcry right now, the media will probably laud Trump for achieving the bare minimum – staying on script, not focusing entirely on himself or not immediately sparking a war. But even more dangerously, they will likely use the speech as chance to report that Trump has finally made a pivot.

We cannot let traditional media outlets normalize Trump. Especially not now. As our friends at Media Matters for America have made clear, Trump is never going to change:

The pivot is not coming. There is no decision this president can make that will alter the trajectory of his administration. It’s long past time for journalists to stop predicting a change in course is imminent, or even possible… Instead of constantly looking for signs of the pivot, journalists should be stressing the remarkable consistency of Trump’s tenure. The administration’s throughline is chaos and hate, failure propagated by laziness and stupidity. Trump told us who he was, and he is living up to it.2

We cannot let pundits race to assert that Trump has finally come around. We must demand that they consistently report that he is a racist extremist who is leading an authoritarian regime focused on destroying the norms and foundations of our democracy, including the free press.3 Can you add your name today?

Tell the mainstream corporate media: Don’t use Trump’s State of the Union address to normalize his dangerous agenda. Click here to sign the petition.

Trump’s State of the Union is coming on the heels of his calling Haiti and African countries shitholes during a meeting to negotiate the fate of the 800,000 DACA recipients whom he threw under the bus in September. Already, six Democrats have said that they are going to skip the State of the Union entirely, ensuring that they don’t provide a shred of legitimacy to his racist regime.

The media should also refuse to provide legitimacy to Trump. We must put massive pressure on them to keep their standards high and refuse to lower them for someone clearly unfit for office. Click the link below to sign the petition:

https://act.credoaction.com/sign/SOTU_Media?t=8&akid=26946%2E11103932%2E02uAIi

Thanks for standing up to Trump,

Heidi Hess, Senior Campaign Manager
CREDO Action from Working Assets

Add your name:

Sign the petition ►

References:

  1. Matthew Yglesias, “Tuesday’s DACA negotiation stunt showed how dangerously we’ve lowered the bar for Trump,” Vox, Jan. 10, 2018.
  2. Matt Gertz, “There will be no pivot,” Media Matters for America, Aug. 16, 2017.
  3. Sabrina Siddiqui, “Donald Trump faces backlash as he reveals ‘Fake News Awards’ winners,” The Guardian, Jan. 18, 2018.
Posted byconnie dello buonoJanuary 24, 2018January 24, 2018Posted inPolitics1 Comment on Low bar set by media for con man

Department of Veterans Affairs Opioid Prescribing Data

via Department of Veterans Affairs Opioid Prescribing Data

Posted byconnie dello buonoJanuary 24, 2018Posted inMenuLeave a comment on Department of Veterans Affairs Opioid Prescribing Data

Department of Veterans Affairs Opioid Prescribing Data

Department of Veterans Affairs Opioid Prescribing Data

Opiod rx VAOpiod rx

https://www.data.va.gov/story/department-veterans-affairs-opioid-prescribing-data

Posted byconnie dello buonoJanuary 24, 2018Posted inhealth data, Menu2 Comments on Department of Veterans Affairs Opioid Prescribing Data

Foods to eat to prevent flu or when you have flu to lessen its duration

Vitamin C, Vitamin D, zinc, glutamine, and sulfur rich foods such as garlic, onions, asparagus, yellow and red colored fruits and vegetables.

For AgeLOC Youth, join us free at http://www.nuskin.com with sponsor ID: USW9578356   and search for Pharmanex supplements.

Rest, massage and hydration help in making your immune system strong.

Top 15 Glutamine-Rich Foods You Should Add To Your Diet

http://www.stylecraze.com › Health and Wellness

Dec 27, 2017 – Glutamine is the most abundant non-essential amino acid in your body (1). Research says that it boosts immunity, prevents muscle loss, speeds up muscle recovery due to illnesses or flesh wounds, and improves digestion (2), (3). But its levels can take a dip due to severe illness (cancer or AIDS), strenuous …

L-Glutamine and Diet – Sources of L-Glutamine | Weight Loss and …

https://atlantamedicalinstitute.com/…/l-glutamine-and-diet-sources-of-l-glutamine.html

The dietary sources of glutamine includes especially the protein-rich foods like beef, chicken, fish, dairy products, eggs, vegetables like beans, beets, cabbage, spinach, carrots, parsley, vegetable juices and also in wheat, papaya, brussel sprouts, celery, kale and fermented foods like miso.

8 Glutamine Rich Foods and How They Can Boost Your Muscle …

https://food.ndtv.com/food…/8-glutamine-rich-foods-and-how-they-can-boost-your-&#8230;

Jan 23, 2017 – Here is a list of glutamine rich foods that you could consume in case of lack of this highly essential protein.

All About Glutamine | Precision Nutrition

https://www.precisionnutrition.com/all-about-glutamine

What you should know about glutamine. Where to find it. Cabbage and beets contain high concentrations of glutamine. (Eastern European grandmothers everywhere, rejoice! You have one more reason to encourage your “too skinny” grandchildren to eat the buraczki, borscht and holubtsi!) Otherfood sources include fish, …

Food Containing Glutamine | Live Well – Jillian Michaels

https://livewell.jillianmichaels.com › Diet & Nutrition

As an amino acid, glutamine is a building block of proteins, which are essential to maintain and build muscle mass. Consequently, glutamine is important to bodybuilders and other athletes as a muscle-promoting nutrient. Glutamine is available as a supplement, although some foods naturally contain it and are good sources …

Which foods are high in glutamine? | Exercise.com Blog

https://www.exercise.com/blog/which-foods-are-high-in-glutamine/

Are there foods that have more glutamine than others? Animal proteins are very rich in glutamine and have high concentrations of the amino acid. Some of the best sources of animal proteins withglutamine are dairy products. These include milk, ricotta cheese, yogurt and cottage cheese. The meatsources that are high in …

Posted byconnie dello buonoJanuary 24, 2018Posted inMenuTags:flu, foodsLeave a comment on Foods to eat to prevent flu or when you have flu to lessen its duration

Opioid public health emergency and people with disabilities

NIDILRR is seeking input on the following areas related to the opioid public health emergency and people with disabilities. People with disabilities often experience chronic pain and, as a result, sometimes use opioids to address their pain. We are interested in understanding:

1) whether people with disabilities have been diagnosed and are being treated for an opioid use disorder, and

2) are clinics or community organizations observing a sizeable population of people with disabilities seeking treatment for opioid use disorder?

If so, are current treatment strategies adequate and, if applicable, how is your organization adapting treatment strategies for people with disabilities?

← Back

Thank you for your response. ✨

Posted byconnie dello buonoJanuary 23, 2018Posted inMenuLeave a comment on Opioid public health emergency and people with disabilities

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