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sting increase the risk for another intense wave of COVID-19 in Massachusetts, particularly among vulnerable communities.

These analyses indicate that despite objectives to promote equity and enhance epidemic control in vulnerable communities, testing resources across Massachusetts have been disproportionally allocated to more affluent communities. Worsening structural inequities in access to SARS-CoV-2 testing increase the risk for another intense wave of COVID-19 in Massachusetts, particularly among vulnerable communities.Rapid tests to evaluate SARS-CoV-2-specific T cell responses are urgently needed to decipher protective immunity and aid monitoring vaccine-induced immunity. Using a rapid whole blood assay requiring minimal amount of blood, we measured qualitatively and quantitatively SARS-CoV-2-specific CD4 T cell responses in 31 healthcare workers, using flow cytometry. 100% of COVID-19 convalescent participants displayed a detectable SARS-CoV-2-specific CD4 T cell response. SARS-CoV-2-responding cells were also detected in 40.9% of participants with no COVID-19-associated symptoms or who tested PCR negative. Phenotypic assessment indicated that, in COVID-19 convalescent participants, SARS-CoV-2 CD4 responses displayed an early differentiated memory phenotype with limited capacity to produce IFNγ. Conversely, in participants with no reported symptoms, SARS-CoV-2 CD4 responses were enriched in late differentiated cells, co-expressing IFNγ and TNFα and also Granzyme B. This proof of concept study presents a scalable alternative to PBMC-based assays to enumerate and phenotype SARS-CoV-2-responding T cells, thus representing a practical tool to monitor adaptive immunity in vaccine trials.

In this proof of concept study, we show that SARS-CoV-2 T cell responses are easily detectable using a rapid whole blood assay requiring minimal blood volume. Such assay could represent a suitable tool to monitor adaptive immunity in vaccine trials.

In this proof of concept study, we show that SARS-CoV-2 T cell responses are easily detectable using a rapid whole blood assay requiring minimal blood volume. Such assay could represent a suitable tool to monitor adaptive immunity in vaccine trials.As a safe exposure level for fluoride in pregnancy has not been established, we used data from two prospective studies for benchmark dose modeling. We included mother-child pairs from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort in Mexico and the Maternal-Infant Research on Environmental Chemicals (MIREC) cohort in Canada. Children were assessed for IQ at age 4 (n=211) and between 6 and 12 years (n=287) in the ELEMENT cohort and between ages 3 and 4 years (n=512) in the MIREC cohort. We calculated covariate-adjusted regression coefficients and their standard errors to explore the concentration-effect function for maternal urinary fluoride with children's IQ, including possible sex-dependence. Assuming a benchmark response of 1 IQ point, we derived benchmark concentrations (BMCs) of maternal urinary fluoride and benchmark concentration levels (BMCLs). No deviation from linearity was detected from the results of the two studies. selleck Using a linear slope, the BMC for maternal urinary fluoride associated with a 1-point decrease in IQ scores of preschool-aged boys and girls was 0.29 mg/L (BMCL, 0.18 mg/L). The BMC was 0.30 mg/L (BMCL, 0.19 mg/L) when pooling the IQ scores from the older ELEMENT children and the MIREC cohort. Boys showed slightly lower BMC values compared with girls. Relying on two prospective studies, maternal urine-fluoride exposure at levels commonly occurring in the general population, the joint data showed BMCL results about 0.2 mg/L. These results can be used to guide decisions on preventing excess fluoride exposure in vulnerable populations.Centhaquine is a novel, first-in-class resuscitative agent for the treatment of hypovolemic shock. Efficacy of centhaquine for the treatment of hypovolemic shock as an adjuvant to standard of care (SOC) was evaluated in a prospective, multi-center, randomized, double-blind, controlled phase III study. Key inclusion criteria were, systolic blood pressure (SBP) of ≤90 mm Hg, blood lactate levels of ≥2 mmol/L and patients receiving SOC in a hospital or ICU setting. Primary endpoints of the study were change in SBP, diastolic blood pressure (DBP), blood lactate levels and base deficit. Mortality through day 28 was the key secondary endpoint. A total of 197 patients were screened, of which 105 patients met eligibility criteria and were included in the study. Patients were randomized in a 21 ratio, 71 patients to centhaquine (0.01 mg/kg, IV infusion) group and 34 patients to control group. SOC was provided to both centhaquine and control groups. Demographics were similar in both groups, except patients in saline grimproved in centhaquine but not in control group in the first 6 hours of resuscitation. Centhaquine improved acute respiratory distress syndrome (ARDS) and multiple organ dysfunction score (MODS). No drug related adverse event was reported. Centhaquine (Lyfaquin®) is a highly efficacious resuscitative agent for the treatment of hypovolemic shock as an adjuvant to SOC.

To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas.

A dataset of 4806 cataract patients were gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay, and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction.

When the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs ± SD (in Diopters) in the testing set were 0.356 ± 0.329 for Haigis, 0.352 ± 0.319 for Hoffer Q, 0.371 ± 0.336 for Holladay, and 0.361 ± 0.331 for SRK/T which were significantly lower than those of the original formulas 0.373 ± 0.328 for Haigis, 0.408 ± 0.337 for Hoffer Q, 0.384 ± 0.341 for Holladay, and 0.394 ± 0.351 for SRK/T.

Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.

Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, physiologically-relevant mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus, and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following calibration with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we conducted global sensitivity analysis of model parameters and ranked them for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination was simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy following infection.Background Environmental chemical exposures can affect telomere length, which in turn has been associated with adverse health outcomes including cancer. Firefighters are occupationally exposed to many hazardous chemicals and have higher rates of certain cancers. As a potential marker of effect, we assessed associations between chemical exposures and telomere length in women firefighters and office workers from San Francisco, CA. Methods We measured serum levels of polyfluoroalkyl substances (PFAS), urinary metabolites of flame retardants, including organophosphate flame retardants (OPFRs), and telomere length in peripheral blood leukocytes in women firefighters and office workers who participated in the 2014-15 Women Workers Biomonitoring Collaborative. Multiple linear regression models were used to assess associations between chemical exposures and telomere length. Results Regression results revealed significant positive associations between perfluorooctanoic acid (PFOA) and telomere length and perfluorooctare between the two groups and/or potential unmeasured confounding. Conclusion Our findings suggest positive associations between PFAS and telomere length in women workers, with larger effects seen among firefighters as compared to office workers. The OPFR metabolites BDCPP and BCEP are also associated with telomere length in firefighters and office workers. Associations between chemical exposures and telomere length reported here and by others suggest mechanisms by which these chemicals may affect carcinogenesis and other adverse health outcomes.