Inference of Antibiotic Resistance and Virulence among Diverse Group A Streptococcus Strains Using emm Sequencing and Multilocus Genotyping Methods

typing (direct sequencing of the genomic segment coding for the antigenic portion of the M protein) or by multilocus genotyping methods. Phenotype analysis, including critical AbR typing, is generally achieved by much slower and more laborious direct culture-based methods. type and the associated AbR and virulence phenotypes. types

Inverse relationships between <i>emm</i> groups and <i>emm</i> types.

<p><i>emm</i> groups (set of equally likely <i>emm</i> types, as identified by T-5000 PCR/ESI-MS analysis) associated with each <i>emm</i> type (identified by <i>emm</i> gene sequence analysis); Contradictory identifications are shown in bold. All identities are shown as “identity (number identified)” for that specific associative category.</p

Rates and significance of nonrandom distribution by sequenced <i>emm</i> type.

<p>Overall rate of antibiotic resistance among all GAS <i>emm</i> types, and the significance of nonrandom association between <i>emm</i> type and antibiotic resistance.^aIncludes both intermediate and fully resistant isolates, as per CLSI definitions. <i>p</i> values represent the probability that the observed distribution of resistance among the types resulted from a random distribution among the real population. Pearson tests were run using Monte Carlo method; N = 10,000.</p><p>ERY, erythromycin; CLI, clindamycin; TET, tetracycline; LEV, levofloxacin; OFX, ofloxacin; CHL, chloramphenicol.</p

Isolate properties by PCR/ESI-MS <i>emm</i> group.

<p>Observed rates of antibiotic resistance and scarlet fever exotoxin gene carriage for all identified PCR-ESI/MS emm groups. N+(N) = Number of Positives (Number Tested). Bold indicates 50% or greater rates of resistance. Positives either carry a particular <i>spe</i> gene or show resistance to a particular antibiotic (inclusive of both intermediate and full resistance). ERY, erythromycin; CLI, clindamycin; TET, tetracycline; LEV, levofloxacin; CHL, chloramphenicol; OFX, ofloxacin.^aIndependently significant specific association, positive or negative. ^bSignificant after alpha (Bonferroni) adjustment for multiple measures (highly significant). <i>speA</i>, scarlet fever exotoxin gene A; <i>speC</i>, scarlet fever exotoxin gene C. These following results are not shown in the table: 100% of tested isolates (in all cases N>300) were susceptible to penicillin (PEN), vancomycin (VAN), cefepime (CPM), cefotaxime (CTX), ceftriaxone (CTR), linizolid (LNZ), meropenem (MEM), and gatifloxacin (GAT). 311 were tested with quinupristin-dalfopristim (SYN) and one was resistant, six isolates were tested with trovafloxacin (TVA) and 3 were resistant.</p

Isolate properties by sequenced <i>emm</i> type.

<p>Observed rates of antibiotic resistance and scarlet fever exotoxin gene carriage for all identified sequence-based <i>emm</i> types. N+(N) = Number of Positives (Number Tested). Bold indicates 50% or greater rates of resistance. Positives either carry a particular <i>spe</i> gene or show resistance to a particular antibiotic (inclusive of both intermediate and full resistance). ERY, erythromycin; CLI, clindamycin; TET, tetracycline; LEV, levofloxacin; CHL, chloramphenicol; OFX, ofloxacin.^aIndependently significant specific association, positive or negative. ^bSignificant after alpha (Bonferroni) adjustment for multiple measures (highly significant). <i>speA</i>, scarlet fever exotoxin gene A; <i>speC</i>, scarlet fever exotoxin gene C. These following results are not shown in the table: 100% of tested isolates (in all cases N>300) were susceptible to penicillin (PEN), vancomycin (VAN), cefepime (CPM), cefotaxime (CTX), ceftriaxone (CTR), linizolid (LNZ), meropenem (MEM), and gatifloxacin (GAT). 311 were tested with quinupristin-dalfopristim (SYN) and one was resistant, six isolates were tested with trovafloxacin (TVA) and 3 were resistant.</p

Correlations between <i>emm</i> groups and <i>emm</i> types.

<p><i>emm</i> types (identified by <i>emm</i> gene sequence analysis) associated with each <i>emm</i> group (set of equally likely <i>emm</i> types, as identified by T-5000 PCR/ESI-MS analysis); Contradictory identifications are shown in bold. All identities are shown as “identity (number identified)” for that specific associative category.</p

Rates and significance of nonrandom distribution by PCR/ESI-MS <i>emm</i> group.

<p>Overall rate of antibiotic resistance among all GAS <i>emm</i> groups, and the significance of nonrandom association between <i>emm</i> group and antibiotic resistance. ^aIncludes both intermediate and fully resistant isolates, as per CLSI definitions. <i>p</i> values represent the probability that the observed distribution of resistance among the types resulted from a random distribution among the real population. Pearson tests were run using Monte Carlo method; N = 10,000.</p><p>ERY, erythromycin; CLI, clindamycin; TET, tetracycline; LEV, levofloxacin; OFX, ofloxacin; CHL, chloramphenicol.</p

El bosque seco tropical en Colombia

Más de cuarenta investigadores nacionales e internacionales de más de 20 instituciones contribuyeron a este libro que recoge el estado del conocimiento sobre la biodiversidad, el estado de fragmentación y transformación, y la restauración del bosque seco en Colombia. Sin duda nos quedaron por fuera investigadores e instituciones que realizan un trabajo muy valioso en el BST. Con este libro queremos extenderles la invitación a ellos y a todos los demás interesados en BST para que continúen el trabajo en este ecosistema que es clave para la conservación de la biodiversidad y la provisión de servicios ecosistémicos en áreas secas de Colombia. Sobretodo porque hasta ahora empezamos a entender la importancia ecológica de estos ecosistemas.Bogotá, D. C.Instituto de Investigación de Recursos Biológicos Alexander von Humbold

The impact of COVID-19 on the well-being and cognition of older adults living in the United States and Latin America.

In the COVID-19 pandemic, older adults from vulnerable ethnoracial groups are at high risk of infection, hospitalization, and death. We aimed to explore the pandemic's impact on the well-being and cognition of older adults living in the United States (US), Argentina, Chile, Mexico, and Peru. 1,608 (646 White, 852 Latino, 77 Black, 33 Asian; 72% female) individuals from the US and four Latin American countries aged ≥ 55 years completed an online survey regarding well-being and cognition during the pandemic between May and September 2020. Outcome variables (pandemic impact, discrimination, loneliness, purpose of life, subjective cognitive concerns) were compared across four US ethnoracial groups and older adults living in Argentina, Chile, Mexico, and Peru. Mean age for all participants was 66.7 ( = 7.7) years and mean education was 15.4 ( = 2.7) years. Compared to Whites, Latinos living in the US reported greater economic impact ( < .001,  = .031); while Blacks reported experiencing discrimination more often ( < .001,  = .050). Blacks and Latinos reported more positive coping ( < .001,  = 040). Compared to Latinos living in the US, Latinos in Chile, Mexico, and Peru reported greater pandemic impact, Latinos in Mexico and Peru reported more positive coping, Latinos in Argentina, Mexico, and Peru had greater economic impact, and Latinos in Argentina, Chile, and Peru reported less discrimination. The COVID-19 pandemic has differentially impacted the well-being of older ethnically diverse individuals in the US and Latin America. Future studies should examine how mediators like income and coping skills modify the pandemic's impact. Massachusetts General Hospital Department of Psychiatry