There is a critical need for adjuvants that can safely elicit potent and durable T cell-based immunity to intracellular pathogens. Here, we report that parenteral vaccination with a carbomer-based ...adjuvant, Adjuplex (ADJ), stimulated robust CD8 T-cell responses to subunit antigens and afforded effective immunity against respiratory challenge with a virus and a systemic intracellular bacterial infection. Studies to understand the metabolic and molecular basis for ADJ's effect on antigen cross-presentation by dendritic cells (DCs) revealed several unique and distinctive mechanisms. ADJ-stimulated DCs produced IL-1β and IL-18, suggestive of inflammasome activation, but in vivo activation of CD8 T cells was unaffected in caspase 1-deficient mice. Cross-presentation induced by TLR agonists requires a critical switch to anabolic metabolism, but ADJ enhanced cross presentation without this metabolic switch in DCs. Instead, ADJ induced in DCs, an unique metabolic state, typified by dampened oxidative phosphorylation and basal levels of glycolysis. In the absence of increased glycolytic flux, ADJ modulated multiple steps in the cytosolic pathway of cross-presentation by enabling accumulation of degraded antigen, reducing endosomal acidity and promoting antigen localization to early endosomes. Further, by increasing ROS production and lipid peroxidation, ADJ promoted antigen escape from endosomes to the cytosol for degradation by proteasomes into peptides for MHC I loading by TAP-dependent pathways. Furthermore, we found that induction of lipid bodies (LBs) and alterations in LB composition mediated by ADJ were also critical for DC cross-presentation. Collectively, our model challenges the prevailing metabolic paradigm by suggesting that DCs can perform effective DC cross-presentation, independent of glycolysis to induce robust T cell-dependent protective immunity to intracellular pathogens. These findings have strong implications in the rational development of safe and effective immune adjuvants to potentiate robust T-cell based immunity.
Background:
Despite standardization in disease assessments and curative interventions for childhood cancer, palliative assessments and psychosocial interventions remain diverse and disparate.
Aim:
...Identify current approaches to palliative care in the pediatric oncology setting to inform development of comprehensive psychosocial palliative care standards for pediatric and adolescent patients with cancer and their families. Analyze barriers to implementation and enabling factors.
Design:
Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines framed the search strategy and reporting. Data analysis followed integrative review methodology.
Data sources:
Four databases were searched in May 2014 with date restrictions from 2000 to 2014: PubMed, Cochrane, PsycINFO, and Scopus. A total of 182 studies were included for synthesis. Types of studies included randomized and non-randomized trials with or without comparison groups, qualitative research, prior reviews, expert opinion, and consensus report.
Results:
Integration of patient, parent, and clinician perspectives on end-of-life needs as gathered from primary manuscripts (using NVivo coding for first-order constructs) revealed mutual themes across stakeholders: holding to hope, communicating honestly, striving for relief from symptom burden, and caring for one another. Integration of themes from primary author palliative care outcome reports (second-order constructs) revealed the following shared priorities in cancer settings: care access; cost analysis; social support to include primary caregiver support, sibling care, bereavement outreach; symptom assessment and interventions to include both physical and psychological symptoms; communication approaches to include decision-making; and overall care quality.
Conclusion:
The study team coordinated landmark psychosocial palliative care papers into an informed conceptual model (third-order construct) for approaching pediatric palliative care and psychosocial support in oncology settings.
Affect sharing and prosocial motivation are integral parts of empathy that are conceptually and mechanistically distinct. We used a neurodegenerative disease (NDG) lesion model to examine the neural ...correlates of these two aspects of real-world empathic responding. The study enrolled 275 participants, including 44 healthy older controls and 231 patients diagnosed with one of five neurodegenerative diseases (75 Alzheimer's disease, 58 behavioral variant frontotemporal dementia (bvFTD), 42 semantic variant primary progressive aphasia (svPPA), 28 progressive supranuclear palsy, and 28 non-fluent variant primary progressive aphasia (nfvPPA). Informants completed the Revised Self-Monitoring Scale's Sensitivity to the Expressive Behavior of Others (RSMS-EX) subscale and the Interpersonal Reactivity Index's Empathic Concern (IRI-EC) subscale describing the typical empathic behavior of the participants in daily life. Using regression modeling of the voxel based morphometry of T1 brain scans prepared using SPM8 DARTEL-based preprocessing, we isolated the variance independently contributed by the affect sharing and the prosocial motivation elements of empathy as differentially measured by the two scales. We found that the affect sharing component uniquely correlated with volume in right>left medial and lateral temporal lobe structures, including the amygdala and insula, that support emotion recognition, emotion generation, and emotional awareness. Prosocial motivation, in contrast, involved structures such as the nucleus accumbens (NaCC), caudate head, and inferior frontal gyrus (IFG), which suggests that an individual must maintain the capacity to experience reward, to resolve ambiguity, and to inhibit their own emotional experience in order to effectively engage in spontaneous altruism as a component of their empathic response to others.
•Human lesion models can reveal the necessary and sufficient neuroanatomy of empathy.•Affect sharing (AS) and prosocial motivation (PM) derive from distinct neural circuits.•Temporal structures mediate emotion recognition/generation/awareness supporting AS.•Frontal-subcortical structures mediate reward/inhibition of emotion supporting PM.
Abstract
Although recent scientific advances have improved our understanding of basic biological mechanisms underlying chemotherapy-induced peripheral neuropathy (CIPN), few interventions are ...available to prevent or treat CIPN. Although some biological targets from preclinical studies show promise in nonhuman animal models, few targets have been translated to successful clinical trials. To address this problem, the National Cancer Institute’s Symptom Management and Health-Related Quality of Life Steering Committee convened a meeting of experts in the CIPN and oncology symptom management fields to participate in a Clinical Trials Planning Meeting (CTPM). Investigators presented data from preclinical and translational studies for possible CIPN interventions; these were evaluated for readiness of randomized clinical trial testing by experts, and recommendations were provided. Breakout sessions were convened to discuss and develop future studies. The CTPM experts concluded that there is compelling evidence to move forward with selected pharmacological and nonpharmacological clinical trials for the prevention and treatment of CIPN. Several key feasibility issues need to be addressed, however. These include identification of optimal outcome measures to define the CIPN phenotype, establishment of parameters that guide the evaluation of clinically meaningful effects, and adoption of approaches for inclusion of translational and biomarker and/or genetic measures. The results of the CTPM provide support for conducting clinical trials that include both pharmacological and nonpharmacological approaches, alone or in combination, with biomarkers, genetics, or other measures designed to inform underlying CIPN mechanisms. Several working groups were formed to design rigorous CIPN clinical trials, the results of which are ongoing.
Abstract
Objective
Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately ...and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).
Materials and Methods
We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the Unified Medical Language System. Along with health care utilization, aggregated ICD and NLP counts were jointly analyzed by fitting an ensemble of latent mixture models. The multimodal automated phenotyping (MAP) algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying participants with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort phenome-wide association studies (PheWAS) for 2 single nucleotide polymorphisms with known associations.
Results
The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes.
Conclusion
The MAP approach increased the accuracy of phenotype definition while maintaining scalability, thereby facilitating use in studies requiring large-scale phenotyping, such as PheWAS.
Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased ...the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).
The majority of bacteria detected in the nostril microbiota of most healthy adults belong to three genera: Propionibacterium, Corynebacterium, and Staphylococcus. Among these staphylococci is the ...medically important bacterium Staphylococcus aureus. Almost nothing is known about interspecies interactions among bacteria in the nostrils. We observed that crude extracts of cell-free conditioned medium from Propionibacterium spp. induce S. aureus aggregation in culture. Bioassay-guided fractionation implicated coproporphyrin III (CIII), the most abundant extracellular porphyrin produced by human-associated Propionibacterium spp., as a cause of S. aureus aggregation. This aggregation response depended on the CIII dose and occurred during early stationary-phase growth, and a low pH (~4 to 6) was necessary but was not sufficient for its induction. Additionally, CIII induced plasma-independent S. aureus biofilm development on an abiotic surface in multiple S. aureus strains. In strain UAMS-1, CIII stimulation of biofilm depended on sarA, a key biofilm regulator. This study is one of the first demonstrations of a small-molecule-mediated interaction among medically relevant members of the nostril microbiota and the first description of a role for CIII in bacterial interspecies interactions. Our results indicate that CIII may be an important mediator of S. aureus aggregation and/or biofilm formation in the nostril or other sites inhabited by Propionibacterium spp. and S. aureus. Importance: Very little is known about interspecies interactions among the bacteria that inhabit the adult nostril, including Staphylococcus aureus, a potential pathogen that colonizes about a quarter of adults. We demonstrated that coproporphyrin III (CIII), a diffusible small molecule excreted by nostril- and skin-associated Propionibacterium spp., induces S. aureus aggregation in a manner dependent on dose, growth phase, and pH. CIII also induces S. aureus to form a plasma-independent surface-attached biofilm. This report is the first description of a role for CIII in bacterial interspecies interactions at any human body site and a novel demonstration that nostril microbiota physiology is influenced by small-molecule-mediated interactions.
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Electronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for ...comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data.
In this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers.
In terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain.
The SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.
The risk factors associated with the stages of Coronavirus Disease-2019 (COVID-19) disease progression are not well known. We aim to identify risk factors specific to each state of COVID-19 ...progression from SARS-CoV-2 infection through death.
We included 648,202 participants from the Veteran Affairs Million Veteran Program (2011-). We identified characteristics and 1,809 ICD code-based phenotypes from the electronic health record. We used logistic regression to examine the association of age, sex, body mass index (BMI), race, and prevalent phenotypes to the stages of COVID-19 disease progression: infection, hospitalization, intensive care unit (ICU) admission, and 30-day mortality (separate models for each). Models were adjusted for age, sex, race, ethnicity, number of visit months and ICD codes, state infection rate and controlled for multiple testing using false discovery rate (≤0.1). As of August 10, 2020, 5,929 individuals were SARS-CoV-2 positive and among those, 1,463 (25%) were hospitalized, 579 (10%) were in ICU, and 398 (7%) died. We observed a lower risk in women vs. men for ICU and mortality (Odds Ratio (95% CI): 0.48 (0.30-0.76) and 0.59 (0.31-1.15), respectively) and a higher risk in Black vs. Other race patients for hospitalization and ICU (OR (95%CI): 1.53 (1.32-1.77) and 1.63 (1.32-2.02), respectively). We observed an increased risk of all COVID-19 disease states with older age and BMI ≥35 vs. 20-24 kg/m2. Renal failure, respiratory failure, morbid obesity, acid-base balance disorder, white blood cell diseases, hydronephrosis and bacterial infections were associated with an increased risk of ICU admissions; sepsis, chronic skin ulcers, acid-base balance disorder and acidosis were associated with mortality.
Older age, higher BMI, males and patients with a history of respiratory, kidney, bacterial or metabolic comorbidities experienced greater COVID-19 severity. Future studies to investigate the underlying mechanisms associated with these phenotype clusters and COVID-19 are warranted.
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