Little is known of the molecular mechanisms that trigger oligodendrocyte death and demyelination in many acute central nervous system insults. Since oligodendrocytes express functional alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)/kainate-type glutamate receptors, we examined the possibility that oligodendrocyte death can be mediated by glutamate receptor overactivation. Oligodendrocytes in primary cultures from mouse forebrain were selectively killed by low concentrations of AMPA, kainate or glutamate, or by deprivation of oxygen and glucose. This toxicity could be blocked by the AMPA/kainate receptor antagonist 6-nitro-7-sulfamoylbenzo(f)quinoxaline-2,3-dione (NBQX). In vivo, differentiated oligodendrocytes in subcortical white matter expressed AMPA receptors and were selectively injured by microstereotaxic injection of AMPA but not NMDA. These data suggest that oligodendrocytes share with neurons a high vulnerability to AMPA/kainate receptor-mediated death, a mechanism that may contribute to white matter injury in CNS disease.

COVID-19-associated deaths were reported in the United States (1). Understanding the demographic and clinical characteristics of decedents could inform medical and public health interventions focused on preventing COVID-19-associated mortality. This report describes decedents with laboratory-confirmed infection with SARS-CoV-2, the virus that causes COVID-19, using data from 1) the standardized CDC case-report form (case-based surveillance) (https://www.cdc.gov/coronavirus/2019-ncov/php/ reporting-pui.html) and 2) supplementary data (supplemental surveillance), such as underlying medical conditions and location of death, obtained through collaboration between CDC and 16 public health jurisdictions (15 states and New York City). Case-based surveillanceDemographic and clinical data about COVID-19 cases are reported to CDC from 50 states, the District of Columbia, New York City, and U.S. territories using a standardized case-report form (case-based surveillance) or in aggregate. Data on 52,166 deaths from 47 jurisdictions among persons with laboratoryconfirmed COVID-19 were reported individually to CDC via case-based surveillance during February 12-May 18, 2020. Among the 52,166 decedents, 55.4% were male, 79.6% were aged ≥65 years, 13.8% were Hispanic/Latino (Hispanic), 21.0% were black, 40.3% were white, 3.9% were Asian, 0.3% were American Indian/Alaska Native (AI/AN), 0.1% were Native Hawaiian or other Pacific Islander (NHPI), 2.6% were multiracial or other race, and race/ethnicity was unknown for 18.0%. (Table 1). Median decedent age was 78 years (interquartile range (IQR) = 67-87 years). Because information about underlying medical conditions was missing for the majority of these decedents (30,725; 58.9%), data regarding medical conditions were not analyzed further using the case-based surveillance data set. Because most decedents reported to the supplementary data program were also reported to case-based surveillance, no statistical comparisons of the decedent characteristics between the data sets were made. * Underlying medical conditions include cardiovascular disease (congenital heart disease, coronary artery disease, congestive heart failure, hypertension, cerebrovascular accident/stroke, valvular heart disease, conduction disorders or dysrhythmias, other cardiovascular disease); diabetes mellitus; chronic lung disease (chronic obstructive pulmonary disease/emphysema, asthma, tuberculosis, other chronic lung diseases); immunosuppression (cancer, human immunodeficiency virus (HIV) infection, identified as being immunosuppressed); chronic kidney disease (chronic kidney disease, end-stage renal disease, other kidney diseases); neurologic conditions (dementia, seizure disorder, other neurologic conditions); chronic liver disease (cirrhosis, alcoholic hepatitis, chronic liver disease, end-stage liver disease, hepatitis B, hepatitis C, nonalcoholic steatohepatitis, other chronic liver diseases); obesity (body mass index ≥30 kg/m 2 ). Information was collected from decedent medical records or death certificates. ...

, approximately 6.5 million cases of SARS-CoV-2 infection, the cause of coronavirus disease 2019 (COVID-19), and 190,000 SARS-CoV-2-associated deaths have been reported in the United States (1,2). Symptoms associated with SARS-CoV-2 infection are milder in children compared with adults (3). Persons aged <21 years constitute 26% of the U.S. population (4), and this report describes characteristics of U.S. persons in that population who died in association with SARS-CoV-2 infection, as reported by public health jurisdictions. Among 121 SARS-CoV-2-associated deaths reported to CDC among persons aged <21 years in the United States during February 12-July 31, 2020, 63% occurred in males, 10% of decedents were aged <1 year, 20% were aged 1-9 years, 70% were aged 10-20 years, 45% were Hispanic persons, 29% were non-Hispanic Black (Black) persons, and 4% were non-Hispanic American Indian or Alaska Native (AI/AN) persons. Among these 121 decedents, 91 (75%) had an underlying medical condition,* 79 (65%) died after admission to a hospital, and 39 (32%) died at home or in the emergency department (ED). † These data show that nearly three quarters of SARS-CoV-2-associated deaths among infants, children, adolescents, and young adults have occurred in persons aged 10-20 years, with a disproportionate percentage among young adults aged 18-20 years and among Hispanics, Blacks, AI/ANs, and persons with underlying medical conditions. Careful monitoring of SARS-CoV-2 * https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/peoplewith-medical-conditions.html. † Location of death for all cases (121): hospital (79 [65.3%]), home (16 [13.2%]), ED (23 [19.0%]), hospice (one [0.8%]), and unknown (2 [1.7%]).

Residents of long-term care facilities (LTCFs) and health care personnel (HCP) working in these facilities are at high risk for COVID-19-associated mortality. As of March 2021, deaths among LTCF residents and HCP have accounted for almost one third (approximately 182,000) of COVID-19-associated deaths in the United States (1). Accordingly, LTCF residents and HCP were prioritized for early receipt of COVID-19 vaccination and were targeted for on-site vaccination through the federal Pharmacy Partnership for Long-Term Care Program (2). In December 2020, CDC's National Healthcare Safety Network (NHSN) launched COVID-19 vaccination modules, which allow U.S. LTCFs to voluntarily submit weekly facility-level COVID-19 vaccination data.* CDC analyzed data submitted during March 1-April 4, 2021, to describe COVID-19 vaccination coverage among a convenience sample of HCP working in LTCFs, by job category, and compare HCP vaccination coverage rates with social vulnerability metrics of the surrounding community using zip code tabulation area (zip code area) estimates. Through April 4, 2021, a total of 300 LTCFs nationwide, representing approximately 1.8% of LTCFs enrolled in NHSN, reported that 22,825 (56.8%) of 40,212 HCP completed COVID-19 vaccination. † Vaccination coverage was highest among physicians and advanced practice providers (75.1%) and lowest among nurses (56.7%) and aides (45.6%). Among aides (including certified nursing assistants, nurse aides, medication aides, and medication assistants), coverage was lower in facilities located in zip code areas with higher social vulnerability (social and structural factors associated with adverse health outcomes), corresponding to vaccination disparities present in the wider community (3). Additional efforts are needed to improve LTCF immunization policies and practices, build confidence in COVID-19 vaccines, and promote COVID-19 vaccination. CDC and partners have prepared education and training resources to help educate HCP and promote COVID-19 vaccination coverage among LTCF staff members. § *

No abstract

BackgroundThe Médecins Sans Frontières project of Uzbekistan has provided multidrug-resistant tuberculosis treatment in the Karakalpakstan region since 2003. Rates of default from treatment have been high, despite psychosocial support, increasing particularly since programme scale-up in 2007. We aimed to determine factors associated with default in multi- and extensively drug-resistant tuberculosis patients who started treatment between 2003 and 2008 and thus had finished approximately 2 years of treatment by the end of 2010.MethodsA retrospective cohort analysis of multi- and extensively drug-resistant tuberculosis patients enrolled in treatment between 2003 and 2008 compared baseline demographic characteristics and possible risk factors for default. Default was defined as missing ≥60 consecutive days of treatment (all drugs). Data were routinely collected during treatment and entered in a database. Potential risk factors for default were assessed in univariate analysis using chi-square test and in multivariate analysis with logistic regression.Results20% (142/710) of patients defaulted after a median of 6 months treatment (IQR 2.6–9.9). Factors associated with default included severity of resistance patterns (pre-extensively drug-resistant/extensively drug-resistant tuberculosis adjusted odds ratio 0.52, 95%CI: 0.31–0.86), previous default (2.38, 1.09–5.24) and age >45 years (1.77, 1.10–2.87). The default rate was 14% (42/294) for patients enrolled 2003–2006 and 24% (100/416) for 2007–2008 enrolments (p = 0.001).ConclusionsDefault from treatment was high and increased with programme scale-up. It is essential to ensure scale-up of treatment is accompanied with scale-up of staff and patient support. A successful first course of tuberculosis treatment is important; patients who had previously defaulted were at increased risk of default and death. The protective effect of severe resistance profiles suggests that understanding disease severity or fear may motivate against default. Targeted health education and support for at-risk patients after 5 months of treatment when many begin to feel better may decrease default.

This study highlights the need for TB diagnosis at early stages of the disease to minimize APT and decrease the risk of transmission. Additional efforts should concentrate on reducing time to treatment initiation in low-incidence areas and among groups traditionally seen as being at low risk for TB disease.

We combined routinely reported tuberculosis (TB) patient characteristics with genotyping data and measures of geospatial concentration to predict which small clusters (i.e., consisting of only3 TB patients) in the United States were most likely to become outbreaks of at least 6 TB cases. Of 146 clusters analyzed, 16 (11.0%) grew into outbreaks. Clusters most likely to become outbreaks were those in which at least 1 of the first 3 patients reported homelessness or excess alcohol or illicit drug use or was incarcerated at the time of TB diagnosis and in which the cluster grew rapidly (i.e., the third case was diagnosed within 5.3 months of the first case). Of 17 clusters with these characteristics and therefore considered high risk, 9 (53%) became outbreaks. This retrospective cohort analysis of clusters in the United States suggests that routinely reported data may identify small clusters that are likely to become outbreaks and which are therefore candidates for intensified contact investigations.