By L. Ugrasal. Smith College.

The human microbiota order kamagra polo online from canada, a collection of various microorganisms buy kamagra polo online, comprises about 1–3 % of total body mass order kamagra polo online pills, hosting an impressive 100 trillion bacteria, most of which find their niche in the intestine 17 The Impact of Microbiota on Brain and Behavior: Mechanisms & Therapeutic. The majority of the microbial cells are comprised of bacteria from 500 to 1,000 different species varying in diversity and stability, adding over eight million genes to the human genome [2–4]. An individual’s intestinal microbiota outnumbers somatic cells of the human body by approximately a factor of 10 [3], suggesting that an individual organism can no longer be identified as a single entity but rather as a complex ecosystem. Microbes colonize the digestive tract, reaching high numbers following birth and immediately thereafter [5], evolving and dynamically changing throughout one’s lifespan [6]. The importance of microbiota for human health has been known since the early twentieth century, when Metchnikoff, the father of the modern probiotics, hypothesized that rebalancing bacteria in the gut with lactic acid could normalize bowel health and prolong life [7]. We now know that the microbiota plays a crucial role in maintaining physio- logical homeostasis including digestion, metabolism, growth, development and function of the immune system and resistance to pathogens [8–11]. More recently, an increasing volume of evidence has supported the relationship between the enteric microbiota and brain function [11] both in pre-clinical [12–14] and clinical settings [15–17]. Microbiota Throughout Lifespan Although a stable core microbiome is shared among individuals, certain gut micro- bial populations fluctuate over time, depending on several factors such as mode of delivery, feeding regimen, maternal diet/weight, probiotic and prebiotic use and antibiotic exposure pre-, peri- and post-natally [18]. Bacterial colonization follows a relatively consistent pattern, under the influence of a variety of exogenous and endogenous factors. Exogenous factors include exposure to microorganisms from maternal origin such as gut, vaginal canal, or skin but also the environment in general. Endogenous factors encompass the birth delivery mode (vaginally or via cesarean section), gestational age, the type of feeding (breastfeeding or formula), and antibiotic or drug use [19]. The human host-microbe symbiosis is initiated in early life and its establishment is an intriguing and dynamic biological process. The developing microbiome undergoes its own evolution throughout the host’s lifetime, in particular the first 3 years, during which a stable microbiome is established [20–22]. Despite the general dogma that a developing fetus is sterile up until birth [20, 23], increasing evidence suggests that an infant’s initial microbiome might in fact be seeded by its mother prior to birth [24, 25] and is then supported by the presence of maternal microbes during birth [26] and breastfeeding [27, 28]. During and shortly after birth, infants are exposed to microbes mainly originating from the mother [29, 30]. Growing evidence suggests that it is this initial inoculation and subsequent 376 Y. The mode of delivery at birth has recently attracted attention from the scientific community since infants delivered by C-section are more likely to suffer from allergies, asthma and diabetes later in life [21, 31, 32]. Although reasons for these correlations are difficult to tease apart, it has been linked to the crucial role of the early life environment in the development of a healthy microbiome. While the microbial composition of vaginally delivered infants initially resembles that of their mother’s vaginal canal, the microbiota of infants delivered via C-section is more similar to the microbiota of their mother’s skin [26]. Although infants delivered by C-section exhibit a delayed acquisition of the members (Firmicutes and Bacteroidetes) which dominate the adult microbiome, their microbiota composition does eventually match that of their vaginally delivered counterparts in later life [33]. It is currently unclear if birth mode can influence brain development and behavior. In addition to the birth delivery mode, gestational age is thought to contribute to the microbial composition of the host. For example, the microbiota of the pre-term infants lacks two of the main bacterial genera, Bifidobacterium and Lactobacillus, usually present in full-term infants, and instead display a dominance of the Proteobacteria [34]. However, breastfeeding enriched the microbiota of the pre-term infants with the absent microbial species, enhancing the ability of the infant microbiome to utilize human milk oligosaccharides [20]. In addition to the maternal role in the developing infant’s microbiome [35], genetic and environ- mental factors play a role in defining the adult core microbiome. For example, twin studies revealed higher similarities in the microbiota composition between mono- zygotic and dizygotic twins in comparison to other family members, suggesting a significance of the environmental factors over genetics [36, 37] and that microbial ecologies tend to cluster in family members [33]. The contribution of the genetic background and environmental factors to the microbiota of the host and the subsequent functional outcomes remains to be fully elucidated. Knowing that the microbiota can significantly interfere with the human meta- bolic, cognitive, and immune systems, the initiation of the symbiosis especially during prenatal, early postnatal, and adolescence phases appears to be a crucial step for preparing optimal brain development overall and mental health later in life [38– 41]. Consequently, understanding the early interaction between the intestinal microbiota and the host opens new avenues for therapeutic interventions, parti- cularly for infants and young children. Unlike our genetic background, our gut microbiota may be modified in the first 2 years of life and possibly throughout pregnancy via the prenatal diet. The gut microbiome evolves throughout the lifespan and the microbiota diver- sity declines with ageing, shifting in the dominant species but keeping a stable total number of anaerobic bacteria [42, 43]. It has recently been shown that microbial composition of aged individuals correlated with and was influenced by their residential community, dietary regimen and the health status of the individual [44]. Crucially, the loss of community-associated microbiota correlated with 17 The Impact of Microbiota on Brain and Behavior: Mechanisms & Therapeutic. Because of the geographical and ethnic homogeneity of the studied population, future investigations in heterogeneous cohorts are needed to support the importance of the interactions between diet, the microbiota, health and ageing [45]. The complex ecosystem of the host’s microbiota is established at birth and its dynamic nature evolves throughout life span, suggesting its role in maintaining physiological processes potentially via the microbiota-brain-gut axis network. Interdisciplinary Conceptualization of the Microbiota-Brain-Gut-Axis The concept of the microbiota-brain-gut axis is becoming increasingly recognized in scientific research, creating multidisciplinary collaborations in the fields of neuroscience, psychiatry, immunology, gastroenterology and microbiology. The brain-gut axis plays an important role in maintaining homeostasis and its dysfunction has been implicated in various psychiatric and non-psychiatric disorders [46–51]. In addition, modulation of the brain-gut axis is linked to the stress response and altered behavior with the microbiome being an important factor in the brain-gut axis communication network [9, 46, 49, 52–54]. Afferent fibers which project from the gut to cortical centers of the brain such as cerebral, anterior and posterior cingulate, insular, and amygdala cortices and as well as effector fibers projecting to the smooth muscle of the gut are the major routes for bi-directional communication along this axis [55]. Moreover, specific subsets of enteric neurons in the colonic myenteric plexus of rats have recently been shown to be sensitive to microbial manipulation, specifi- cally, a Lactobacillus reuteri strain. A more recent study has shown electro- physiological properties of myenteric neurons are altered in germ-free mice specifi- cally; decreased excitability in myenteric sensory neurons was found in the absence of intestinal microbiota. Upon colonization of germ-free mice with normal gut microbiota, excitability of after-hyperpolarization sensory neurons in germ-free mice was increased [58]. The vagus nerve is the major nerve of the parasympathetic division of the autonomic nervous system, which regulates several vital body functions, including heart rate, gut motility, and bronchial constriction [59, 60]. Microbiota can elicit signals via the vagal nerve to the brain and vice versa [61–63] (Fig. For example, the behavioral effects mediated by two separate probiotic strains in rodents were dependent on intact vagal nerve activation [64]. Similar effects were observed in an animal model of colitis, where anxiolytic effect of Bifidobacterium was absent in vagotomized mice [65]. In contrast to probiotic-mediated effects, antibiotic treatment-induced microbiota alterations in mice did not show a similar dependence on vagal nerve activity [66] suggesting that enteric microbiota communicates with the brain by diverse mechanisms (Fig. In addition to neuroanatomical complexity, neuro- chemistry may play a vital role in modulating microbiota-brain-gut communication. A change the balance of symbionts and pathobionts favoring pathobiont overgrowth, results in dysbiosis. Pathobiont overgrowth leading to perturbations in intestinal microbiota induces inflammation and loss of barrier function (leaky gut), promoting increased translocation of pathogenic bacterial components from the intestinal mucosa to the systemic circulation, where they activate innate immunity characterized by pro- duction of pro-inflammatory cytokines, resulting in systemic inflammation and abnormal gut function. Alterations in serotonin transmission may underlie the pathological symptoms 380 Y. Serotonin synthesis in the brain depends on the availability of its precursor, tryptophan.

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This cause category includes “Causes arising in the perinatal period” as defined in the International Classification of Diseases generic kamagra polo 100mg free shipping, principally low birthweight generic 100 mg kamagra polo otc, prematurity buy discount kamagra polo 100mg, birth asphyxia, and birth trauma, and does not include all causes of deaths occurring in the perinatal period. Communicable, maternal, perinatal, 552,376 169,032 16,353 22,553 32,261 18,503 7,975 4,567 1,387 272,631 and nutritional conditions A. Infectious and parasitic diseases 320,663 78,874 12,391 20,370 30,127 15,899 5,391 2,551 623 166,227 1. Hepatitis Bb 2,082 92 108 225 398 430 111 38 9 1,411 Hepatitis Cb 844 31 44 82 163 182 47 17 4 570 8. Hookworm disease 634 56 237 10 6 8 5 2 1 323 Other intestinal infections 63 16 6 1 1 2 1 0 0 27 Other infectious diseases 38,095 7,065 2,010 1,829 2,442 2,318 1,058 735 213 17,669 B. Respiratory infections 86,710 32,320 2,475 1,080 1,356 1,878 2,299 1,829 698 43,936 1. Lower respiratory infections 83,606 31,654 1,930 1,006 1,274 1,799 2,227 1,786 681 42,357 2. Abortion 3,502 — — — — — — — — — Other maternal conditions 9,308 — — — — — — — — — D. Birth asphyxia and birth trauma 31,429 17,646 0 0 0 0 — — — 17,646 Other perinatal conditions 15,043 7,965 0 0 0 0 — 0 — 7,966 180 | Global Burden of Disease and Risk Factors | Colin D. Noncommunicable diseases 678,483 40,662 12,508 39,898 48,592 82,245 62,479 42,709 12,241 341,334 A. Malignant neoplasms 74,753 560 757 1,898 4,465 12,873 11,531 6,552 1,296 39,933 1. Trachea, bronchus, and lung cancers 10,701 3 7 64 564 2,600 2,811 1,481 208 7,738 8. Leukemia 3,965 224 347 636 290 311 214 132 30 2,184 Other malignant neoplasms 7,538 235 162 261 514 1,330 1,106 582 145 4,335 B. Neuropsychiatric conditions 137,074 10,291 5,938 23,898 13,022 7,037 2,731 2,323 949 66,189 1. Unipolar depressive disorders 43,427 0 2,452 5,692 4,992 3,076 906 180 33 17,331 2. Mental retardation, lead-caused 8,599 4,319 17 22 7 4 1 0 0 4,370 Other neuropsychiatric disorders 16,644 5,236 724 762 637 527 303 296 92 8,577 F. Hearing loss, adult onset 24,607 — — 581 4,007 4,331 2,387 887 101 12,293 Other sense organ disorders 42 3 2 2 2 4 2 4 1 20 G. Cardiovascular diseases 178,929 1,417 870 3,522 8,988 24,986 25,652 20,136 6,079 91,650 1. Ischemic heart disease 71,882 103 217 1,033 3,782 12,275 11,574 8,509 2,270 39,761 4. Cerebrovascular disease 62,669 215 146 563 2,055 7,924 9,867 8,000 2,202 30,972 5. Inflammatory heart diseases 5,811 260 80 389 615 756 549 420 153 3,222 Other cardiovascular diseases 22,446 582 237 875 1,648 2,134 1,910 1,862 998 10,248 182 | Global Burden of Disease and Risk Factors | Colin D. Respiratory diseases 58,086 3,254 1,699 2,368 2,918 7,262 6,468 5,466 1,889 31,324 1. Chronic obstructive pulmonary 33,453 48 15 120 1,307 5,359 5,162 4,466 1,499 17,977 disease 2. Asthma 11,514 1,013 1,348 1,783 829 714 285 156 36 6,165 Other respiratory diseases 13,119 2,193 336 464 781 1,188 1,021 845 354 7,182 I. Appendicitis 377 7 28 36 38 55 24 19 6 213 Other digestive diseases 33,591 7,012 612 1,536 2,033 2,829 1,510 1,036 370 16,938 J. Benign prostatic hypertrophy 2,613 — — 0 12 2,118 255 173 55 2,613 Other genitourinary system 4,691 648 62 142 211 395 296 233 84 2,070 diseases K. Low back pain 1,692 69 167 184 212 184 55 23 4 899 Other musculoskeletal disorders 3,905 122 187 448 195 245 178 153 60 1,587 M. Spina bifida 1,488 706 10 4 0 1 0 0 0 721 Other congenital anomalies 4,405 2,196 98 74 21 17 6 3 1 2,417 N. Unintentional injuries 113,235 7,608 12,447 21,335 15,467 9,335 2,931 1,311 338 70,773 1. Other unintentional injuries 41,050 3,219 4,644 7,923 5,096 2,899 972 381 95 25,229 B. War 6,492 91 71 2,628 2,254 563 157 35 12 5,809 Other intentional injuries 317 17 15 104 74 26 11 5 2 253 184 | Global Burden of Disease and Risk Factors | Colin D. Note: — an estimate of zero; the number zero in a cell indicates a non-zero estimate of less than 500. For East Asia and Pacific, Europe and Central Asia, and Latin America and the Caribbean regions, these figures include late effects of polio cases with onset prior to regional certification of polio eradication in 1994, 2000, and 2002, respectively. The Burden of Disease and Mortality by Condition: Data, Methods, and Results for 2001 | 185 Table 3C. Communicable, maternal, perinatal, 76,710 20,685 2,069 3,489 4,693 3,712 2,155 1,345 456 38,605 and nutritional conditions A. Infectious and parasitic diseases 36,941 7,035 1,394 2,822 4,207 3,032 1,626 909 214 21,238 1. Hepatitis Bb 673 17 2 70 179 218 52 10 3 551 Hepatitis Cb 275 4 0 29 76 91 21 4 1 228 8. Hookworm disease 168 16 65 1 1 1 1 0 0 86 Other intestinal infections 14 5 2 0 0 0 0 0 0 7 Other infectious diseases 4,318 825 146 371 433 217 114 138 60 2,302 B. Abortion 191 — — — — — — — — — Other maternal conditions 1,714 — — — — — — — — — D. Birth asphyxia and birth trauma 7,737 4,044 — — — — — — — 4,044 Other perinatal conditions 4,734 2,420 0 — — — — — — 2,420 186 | Global Burden of Disease and Risk Factors | Colin D. Noncommunicable diseases 228,073 10,262 3,138 12,214 16,524 30,234 23,387 16,449 4,848 117,055 A. Leukemia 1,652 98 156 269 109 136 77 45 9 900 Other malignant neoplasms 1,640 57 33 34 72 282 207 111 25 820 B. Neuropsychiatric conditions 42,926 2,395 1,636 7,643 4,601 2,490 1,083 847 354 21,050 1. Mental retardation, lead-caused 2,598 1,335 1 0 0 0 0 0 0 1,336 Other neuropsychiatric disorders 3,255 907 100 227 241 120 82 73 24 1,775 F. Hearing loss, adult onset 8,712 — — 231 1,571 1,580 873 294 29 4,578 Other sense organ disorders 8 0 0 1 1 1 0 0 0 4 G. Cardiovascular diseases 52,872 249 182 951 2,381 6,987 7,874 6,652 2,090 27,365 1. Inflammatory heart diseases 1,147 25 11 52 71 121 123 118 56 577 Other cardiovascular diseases 5,173 119 50 224 345 500 449 461 244 2,392 188 | Global Burden of Disease and Risk Factors | Colin D. Chronic obstructive pulmonary 17,181 7 2 13 308 2,346 2,476 2,724 1,049 8,924 disease 2. Asthma 3,203 254 335 513 267 209 77 46 9 1,709 Other respiratory diseases 3,167 399 54 88 154 258 302 296 169 1,720 I.

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As a measure of country-level labor market rigidity order kamagra polo with american express, we use a 13 composite index of employment law strictness from Botero et al cheap kamagra polo 100 mg fast delivery. We face a similar problem in estimating the effect of government effectiveness: we don’t lack country-level indicators of government effectiveness (e order kamagra polo pills in toronto. As a source of country-level variation, we use the change in the World Bank’s Rule of Law score. To measure how much each sector is dependent on the government, we compute our own measure of sectoral government dependence. The variable Government Dependence is defined, for each sector, as the ratio of total news counts having “government” as the topic to total news for that sector (see table 1 for details). In the online appendix, we show there is no substantial difference in the results whether using, instead of Rule of Law, the change in “Control of corruption” or alternative measures of government efficiency (Chong et al. The validity of this growth accounting exercise relies on the assumption that firms equalize the marginal revenue product of each input to its marginal cost. To see how this could be reflected in our growth accounting framework, consider the following amendment to equation (2. We compute the variable Country Meritocracy as the average of three World Economic Forum executive opinion surveys previously described. In table 3, panel A, column 5 we combine all these interaction variables in one specification. D Robustness Because meritocracy correlates at the country level with many other institutional variables, we want to make sure that the observed effect is really due to meritocracy and not to other factors. For this reason, in table 3, panel C, we repeat the same estimations of table 3A for the sample period 1985–1995. In fact, it even has the opposite sign of the one obtained in the last specification. We do the same for countries, with the top six for meritocracy labelled as “high merit” and the bottom six as “low merit. Also, at the firm level, value added has a different definition than at the sector level, which does not map onto sector-level accounts. At the firm level, these four variables are mapped, respectively, to revenues, fixed 13 assets, labor costs, and residual costs (all costs other than capital and labor). In table 4, we reproduce a similar specification as in table 3, panel A at the firm level. The main difference with respect to the sector-level analysis is that Country Meritocracy is now replaced by Firm Meritocracy (we explain its construction in section 1 and table 1). Apart from the fact that this variable varies at the firm level, a distinct advantage of it is that it reflects factual information about firm characteristics, as opposed to perceptions. As figure 6 shows, Italy exhibits a distribution of this firm-level meritocracy that is much more left-skewed than the other countries in our sample. The firm-level meritocracy is highly correlated with the country- level one (see figure 7). In table 5, column 2, we add, as a control variable, the percentage of employees with a college degree. If that effect exists, it is captured, in our regression, by the country fixed effects. To correct for the attenuation bias of the standard errors-in-variable problem, we need to make an assumption on the reliability of the measurement of the variable Country Meritocracy. Since the squared correlation between the country- level meritocracy and firm-level meritocracy is about 50%, we assume this reliability to be 50%. Thus, the “meritocracy” 19 effect explains between 61% and 83% of the Italian gap. If this is the case, why did Italian firms fail to adopt superior managerial techniques? This explanation has the advantage of containing the hope that, in the long run, the adaptation will take place, even absent policy interventions. If this were the case, then convergence in the long run will not occur without a policy intervention. The most obvious one is that loyalty- based management can function better in environments where legal enforcement is either inefficient or unavailable. Among developed countries, Italy stands out both for its inefficient legal system (the average time to enforce a contract, as measured by Djankov et al. To corroborate this hypothesis, we need to find a way to measure the differential benefit of being loyalty-based in Italy. We focus on three external constraints, namely: financial constraints, labor regulation, and bureaucracy. In table 6, we estimate, using a probit model, the conditional probability that the firm 20 encounters each of these constraints. Beside sector fixed effects, the key explanatory variables are the firm level of meritocracy, and its interaction with a dummy for Italy. The interaction between the meritocracy index and the Italy dummy is very similar in magnitude, but opposite in sign, to the baseline coefficient of meritocracy. Interestingly, this interaction effect for Italy is significant for financial constraints and bureaucratic constraints, but not for labor market constraints. Loyal management can exchange favors with banks and bypass bureaucracy through political connections or bribes, but finds it more difficult to overcome the constraints that labor regulation puts on growth. These results are hardly proof that loyalty-based management is advantageous in Italy, but they are consistent with this assumption. Conclusions In this paper we try to explain why 20 years ago Italian productivity stopped growing. We find no evidence that this slowdown is due to international trade developments. We also do not find any evidence supporting the claim that excessive protection of employees is the cause. In this sense, the Italian disease is an extreme form of the European disease identified by Bloom et al. We find evidence for this hypothesis using both country/sector-level data and firm-level data. Our evidence suggests that even today un-meritocratic managerial practices provide a comparative advantage in the Italian institutional environment. In sum, the explanation for the Italian disease most consistent with the data is that Italy suffers from an extreme form of the European disease identified by Bloom et al. In other words, familyism and cronyism are the ultimate cause of the Italian disease. Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. García-Santana, Manuel, Enrique Moral-Benito, Josep Pijoan-Mas, and Roberto Ramos.