Cytoarchitectureal changes in hippocampal subregions of the NZB/W F1 mouse model of lupus.

Over 50% of clinical patients affected by the systemic lupus erythematosus disease display impaired neurological cognitive functions and psychiatric disorders, a form called neuropsychiatric systemic lupus erythematosus. Hippocampus is one of the brain structures most sensitive to the cognitive deficits and psychiatric disorders related to neuropsychiatric lupus. The purpose of this study was to compare, layer by layer, neuron morphology in lupus mice model NZB/W F1 versus Wild Type mice. By a morphometric of cells identified on Nissl-stained sections, we evaluated structural alterations between NZB/W F1 and Wild Type mice in seven hippocampal subregions: Molecular dentate gyrus, Granular dentate gyrus, Polymorph dentate gyrus, Oriens layer, Pyramidal layer, Radiatum layer and Lacunosum molecular layer. By principal component analysis we distinguished healthy Wild Type from NZB/W F1 mice. In NZB/W F1 mice hippocampal cytoarchitecture, the neuronal cells resulted larger in size and more regular than those of Wild Type. In NZB/W F1, neurons were usually denser than in WT. The Pyramidal layer neurons were much denser in Wild Type than in NZB/W F1. Application of principal component analysis, allowed to distinguish NZB/W F1 lupus mice from healthy, showing as NZBW subjects presented a scattered distribution and intrasubject variability. Our results show a hypertrophy of the NZB/W F1 hippocampal neurons associated with an increase in perikaryal size within the CA1, CA2, CA3 region and the DG. These results help advance our understanding on hippocampal organization and structure in the NZB/W F1 lupus model, suggesting the hypothesis that the different subregions could be differentially affected in neuropsychiatric systemic lupus erythematosus disease. Leveraging an in-depth analysis of the morphology of neural cells in the hippocampal subregions and applying dimensionality reduction using PCA, we propose an efficient methodology to distinguish pathological NZBW mice from WT mice."

Real-world comparative effectiveness of mRNA-1273 and BNT162b2 vaccines among immunocompromised adults in the United States

IntroductionHead-to-head studies comparing COVID-19 mRNA vaccine effectiveness in immunocompromised individuals, who are vulnerable to severe disease are lacking, as large sample sizes are required to make meaningful inferences. MethodsThis observational comparative effectiveness study was conducted in closed administrative claims data from the US HealthVerity database (December 11, 2020-January 10, 2022, before omicron). A 2-dose mRNA-1273 versus BNT162b2 regimen was assessed for preventing medically-attended breakthrough COVID-19 diagnosis and hospitalizations among immunocompromised adults. Inverse probability of treatment weighting was applied to balance baseline characteristics between vaccine groups. Incidence rates from patient-level data and hazard ratios (HRs) using weighted Cox proportional hazards models were calculated. ResultsOverall, 57,898 and 66,981 individuals received a 2-dose regimen of mRNA-1273 or BNT161b2, respectively. Among the weighted population, mean age was 51 years, 53% were female, and baseline immunodeficiencies included prior blood transplant (8%-9%), prior organ transplant (7%), active cancer (12%-13%), primary immunodeficiency (25%-26%), HIV (20%-21%), and immunosuppressive therapy use (60%-61%). Rates per 1,000 person-years (PYs; 95% confidence intervals [CI]s) of breakthrough medically-attended COVID-19 were 25.82 (23.83-27.97) with mRNA-1273 and 30.98 (28.93, 33.18) with BNT162b2 (HR, 0.83; 95% CI, 0.75-0.93). When requiring evidence of an antigen or polymerase chain reaction test before COVID-19 diagnosis, the HR for medically-attended COVID-19 was 0.78 (0.67-0.92). Breakthrough COVID-19 hospitalization rates per 1,000 PYs (95% CI) were 3.66 (2.96-4.51) for mRNA-1273 and 4.68 (3.91-5.59) for BNT162b2 (HR, 0.78; 0.59-1.03). Utilizing open and closed claims for outcome capture only, or both cohort entry/outcome capture, produced HRs (95% CIs) for COVID-19 hospitalization of 0.72 (0.57-0.92) and 0.66 (0.58-0.76), respectively. ConclusionsAmong immunocompromised adults, a 2-dose mRNA-1273 regimen was more effective in preventing medically-attended COVID-19 in any setting (inpatient and outpatient) than 2-dose BNT162b2. Results were similar for COVID-19 hospitalization, although statistical power was limited when using closed claims only. Study RegistrationNCT05366322

Use of an EHR to inform an administrative data algorithm to categorize inpatient COVID-19 severity

STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSAlgorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data (RWD). ObjectiveTo explore use of electronic health record (EHR) data to inform an administrative data algorithm for classification of supplemental oxygen or noninvasive ventilation (O2/NIV) and invasive mechanical ventilation (IMV) to assess disease severity in hospitalized COVID-19 patients. DesignIn this retrospective cohort study, we developed an initial procedure-based algorithm to identify O2/NIV, IMV, and NEITHER O2/NIV nor IMV in two inpatient RWD sources. We then expanded the algorithm to explore the impact of adding diagnoses indicative of clinical need for O2/NIV (hypoxia, hypoxemia) or IMV (acute respiratory distress syndrome) and O2-related patient vitals available in the EHR. Observed changes in severity categorization were used to augment the administrative algorithm. SettingOptum de-identified COVID-19 EHR data and HealthVerity claims and chargemaster data (March - August 2020). ParticipantsAmong patients hospitalized with COVID-19 in each RWD source, our motivating example selected dexamethasone (DEX+) initiators and a random selection of patients who were non-initiators of a corticosteroid of interest (CSI-) matched on date of DEX initiation, age, sex, baseline comorbidity score, days since admission, and COVID-19 severity level (NEITHER, O2/NIV, IMV) on treatment index. Main Outcome and MeasuresInpatient COVID-19 severity was defined using the algorithms developed to classify respiratory support requirements among hospitalized COVID-19 patients (NEITHER, O2/NIV, IMV). Measures were reported as the treatment-specific distributions of patients in each severity level, and as observed changes in severity categorization between the initial procedure-based and expanded algorithms. ResultsIn the administrative data cohort with 5,524 DEX+ and CSI- patient pairs matched using the initial procedure-based algorithm, 30% were categorized as O2/NIV, 5% as IMV, and 65% as NEITHER. Among patients assigned NEITHER via the initial algorithm, use of an expanded algorithm informed by the EHR-based algorithm shifted 54% DEX+ and 28% CSI- to O2/NIV, and 2% DEX+ and 1% CSI- to IMV. Among patients initially assigned O2/NIV, 7% DEX+ and 3% CSI- shifted to IMV. Conclusions and RelevanceApplication of learnings from an EHR-based exploration to our administrative algorithm minimized treatment-differential misclassification of COVID-19 severity.

Correlates of COVID-19 vaccine acceptance, hesitancy and refusal among employees of a safety net California county health system with an early and aggressive vaccination program: Results from a cross-sectional survey

Information on vaccine acceptance among healthcare workers is needed as health professionals provide front line care to COVID-19 patients. We developed and implemented an anonymous internet-based cross-sectional survey with direct solicitation among employees of a safety net health system. Items queried demographic and health-related characteristics, experience with and knowledge of COVID-19, and determinants of decisions to vaccinate. COVID-19 vaccine acceptance groups (acceptors, hesitant, refusers) were defined; an adapted version of the WHO vaccine hesitancy scale was included. The survey demonstrated good reliability (Cronbachs alpha = 0.92 for vaccine hesitancy scale; 0.93 for determinants). General linear and logistic regression methods examined factors which were univariately associated with vaccine hesitancy and vaccine acceptance, respectively. Multivariable models were constructed with stepwise model-building procedures. Race/ethnicity, marital status, job classification, immunocompromised status, flu vaccination and childhood vaccination opinions independently predicted hesitancy scale scores. Gender, education, job classification and BMI independently predicted acceptance, hesitancy and refusal groups. Among hesitant employees, uncertainty was reflected in reports of motivating factors influencing their indecision. Despite a strong employee-support environment and job protection, respondents reported physical and mental health effects. Appreciation of varied reasons for refusing vaccination should lead to culturally sensitive interventions to increase vaccination rates in healthcare workers.

A topic analysis of traditional and social media news coverage of the early COVID-19 pandemic and implications for public health communication

Knowledge gaps may initially exist among scientists, medical and public health professionals during pandemics, which are fertile grounds for misinformation in news media. We characterized and compared COVID-19 coverage in newspapers, television, and social media, and discussed implications for public health communication strategies that are relevant to an initial pandemic response. We conducted a Latent Dirichlet Allocation (LDA), an unsupervised topic modelling technique, analysis of 3,271 newspaper articles, 40 cable news shows transcripts, 96,000 Twitter posts, and 1,000 Reddit posts during March 4 - 12, 2020, a period chronologically early in the timeframe of the COVID-19 pandemic. Coverage of COVID-19 clustered on topics such as epidemic, politics, and the economy, and these varied across media sources. Topics dominating news were not predominantly health-related, suggesting a limited presence of public health in news coverage in traditional and social media. Examples of misinformation were identified particularly in social media. Public health entities should utilize communication specialists to create engaging informational content to be shared on social media sites. Public health officials should be attuned to their target audience to anticipate and prevent spread of common myths likely to exist within a population. This will help control misinformation in early stages of pandemics.