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\"Biocitizenship: The Politics of Bodies, Governance, and Power is a critical study of the relationship between the concept of citizenship and the body\"-- Provided by the publisher.

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

Correlates of protection for COVID-19 vaccines are urgently needed to license additional vaccines. We measured immune responses to four COVID-19 vaccines of proven efficacy using a single serological platform. IgG anti-Spike antibodies were highly correlated with ID50 neutralization in a validated pseudoviral assay and correlated significantly with efficacies for protection against infection with wild-type, alpha and delta variant SARS-CoV-2 virus. The protective threshold for each vaccine was calculated for IgG anti-Spike antibody. The mean protective threshold for all vaccine studies for WT virus was 154 BAU/ml (95 %CI 42–559), and for studies with antibody distributions that enabled precise estimation of thresholds (i.e. leaving out 2-dose mRNA regimens) was 60 BAU/ml (95 %CI 35–102). We propose that the proportion of individuals with responses above the appropriate protective threshold together with the geometric mean concentration can be used in comparative non-inferiority studies with licensed vaccines to ensure that new vaccines will be efficacious.

Thousands of people die while waiting for organ transplants due to a significant gap between demand and supply. This gap often leads to illegal activities and ethical issues such as illicit organ trade and auctions. Therefore, to increase the organ supply and procure more organs, organizations must understand the causes of families who refuse to consent to donate their loved one's organs. Furthermore, such organizations must better identify those families most likely to consent to organ donation. We propose a responsible AI framework that integrates network science and artificial intelligence to identify consent outcomes for organ donation. The proposed framework includes three phases: (1) collecting and pre-processing data, (2) creating new features and identifying root causes of family refusal, and (3) training and testing models to predict the probability of families granting consent for organ donation. The designed artifact included collaborative decisions and network measures, increasing explainability through network science. It integrated human reviews and assessment of risks which increases correct and interpretable predictions. Results can help encourage organ donations and reduce the illegal organ trade. The experimental results show that the designed artifact outperformed previous studies identifying factors affecting consent outcomes. This framework integrates network science and artificial intelligence to reduce maleficence, solve the lack of transparency (i.e., increase trustworthiness) and improve accountability of the model that aims to predict consent outcomes.

Patients with hematological malignancies are at increased risk of severe COVID-19 outcomes due to compromised immune responses, but the insights of these studies have been compromised due to intrinsic limitations in study design. Here we present the PROSECO prospective observational study ( NCT04858568 ) on 457 patients with lymphoma that received two or three COVID-19 vaccine doses. We show undetectable humoral responses following two vaccine doses in 52% of patients undergoing active anticancer treatment. Moreover, 60% of patients on anti-CD20 therapy had undetectable antibodies following full vaccination within 12 months of receiving their anticancer therapy. However, 70% of individuals with indolent B-cell lymphoma displayed improved antibody responses following booster vaccination. Notably, 63% of all patients displayed antigen-specific T-cell responses, which increased after a third dose irrespective of their cancer treatment status. Our results emphasize the urgency of careful monitoring of COVID-19-specific immune responses to guide vaccination schemes in these vulnerable populations.

Sensitive serological testing is essential to estimate the proportion of the population exposed or infected with SARS-CoV-2, to guide booster vaccination and to select patients for treatment with anti-SARS-CoV-2 antibodies. The performance of serological tests is usually evaluated at 14–21 days post infection. This approach fails to take account of the important effect of time on test performance after infection or exposure has occurred. We performed parallel serological testing using 4 widely used assays (a multiplexed SARS-CoV-2 Nucleoprotein (N), Spike (S) and Receptor Binding Domain assay from Meso Scale Discovery (MSD), the Roche Elecsys-Nucleoprotein (Roche-N) and Spike (Roche-S) assays and the Abbott Nucleoprotein assay (Abbott-N) on serial positive monthly samples collected as part of the Co-STARs study ( www.clinicaltrials.gov , NCT04380896) up to 200 days following infection. Our findings demonstrate the considerable effect of time since symptom onset on the diagnostic sensitivity of different assays. Using a time-to-event analysis, we demonstrated that 50% of the Abbott nucleoprotein assays will give a negative result after 175 days (median survival time 95% CI 168–185 days), compared to the better performance over time of the Roche Elecsys nucleoprotein assay (93% survival probability at 200 days, 95% CI 88–97%). Assays targeting the spike protein showed a lower decline over the follow-up period, both for the MSD spike assay (97% survival probability at 200 days, 95% CI 95–99%) and the Roche Elecsys spike assay (95% survival probability at 200 days, 95% CI 93–97%). The best performing quantitative Roche Elecsys Spike assay showed no evidence of waning Spike antibody titers over the 200-day time course of the study. We have shown that compared to other assays evaluated, the Abbott-N assay fails to detect SARS-CoV-2 antibodies as time passes since infection. In contrast the Roche Elecsys Spike Assay and the MSD assay maintained a high sensitivity for the 200-day duration of the study. These limitations of the Abbott assay should be considered when quantifying the immune correlates of protection or the need for SARS-CoV-2 antibody therapy. The high levels of maintained detectable neutralizing spike antibody titers identified by the quantitative Roche Elecsys assay is encouraging and provides further evidence in support of long-lasting SARS-CoV-2 protection following natural infection.

There is no licensed vaccine for the prevention of Klebsiella pneumoniae infections, and increasing levels of antibiotic resistance make this pathogen a high priority for vaccine and therapeutic development. Standardized assays for testing vaccine immunogenicity are paramount for the development of vaccines, and so in this study, we optimized and standardized both antibody-level and function assays for evaluating in-development K. pneumoniae bioconjugate vaccine response in rabbits. Klebsiella pneumoniae , a Gram-negative bacterium, has been listed as a critical pathogen for urgent intervention by the World Health Organization. With no licensed vaccine and increasing resistance to antibiotics, Klebsiella pneumoniae causes a high incidence of hospital- and community-acquired infections. Recently, there has been progress in anti- Klebsiella pneumoniae vaccine development, which has highlighted the lack of standardized assays to measure vaccine immunogenicity. We have developed and optimized methods to measure antibody level and function after vaccination with an in-development Klebsiella pneumoniae O-antigen vaccine. We describe the qualification of a Luminex-based multiplex antibody binding assay and both an opsonophagocytic killing assay and serum bactericidal assay to measure antibody function. Serum from immunized animals were immunogenic and capable of binding to and killing specific Klebsiella serotypes. Cross-reactivity was observed but limited among serotypes sharing antigenic epitopes. In summary, these results demonstrate the standardization of assays that can be used to test new anti- Klebsiella pneumoniae vaccine candidates, which is important for moving them into clinical trials. IMPORTANCE There is no licensed vaccine for the prevention of Klebsiella pneumoniae infections, and increasing levels of antibiotic resistance make this pathogen a high priority for vaccine and therapeutic development. Standardized assays for testing vaccine immunogenicity are paramount for the development of vaccines, and so in this study, we optimized and standardized both antibody-level and function assays for evaluating in-development K. pneumoniae bioconjugate vaccine response in rabbits.

Protection against disease or colonization from serotypes related to those in pneumococcal conjugate vaccines (i.e. cross-protection) vary by serotype; the basis for this variation is not understood. The 13-valent pneumococcal conjugate vaccine (PCV13) replaced 7-valent conjugate (PCV7) in the USA in 2010 allowing assessment of PCV7 and PCV13 immunogenicity and functional cross-protection in vitro. Post-primary, pre-booster and post-booster sera from American Indian children receiving exclusively PCV7 or PCV13 were collected. IgG was measured by ELISA for 13 vaccine serotypes; functional antibody was assessed by opsonophagocytic killing assays for serotypes 6A/B/C and 19A/F. Post-primary IgG geometric mean concentrations (GMC) for serotypes 4 and 9V were lower in PCV13 recipients while 19F GMCs were higher. Only 19F differences persisted after receipt of the booster dose. Functional antibody activity was higher among PCV13 recipients for 6A, 6C, 19A and 19F (p<0.04), and among PCV7 recipients for 6B (p = 0.01). Following PCV7, functional antibodies to 6A but not 19A were observed. High levels of 6C functional activity were seen after PCV13 but not PCV7. Functional antibody activity against 6A/B/C and 19A/F suggest that PCV13 is likely to control the 19A disease and 6C disease remaining despite widespread use of PCV7.

K. pneumoniae is a pathogen that causes serious infections such as pneumonia and sepsis globally. The increasing prevalence of antibiotic resistance in this pathogen has complicated treatment efforts, highlighting the need for preventive therapeutic strategies such as vaccination. However, no licensed vaccines are currently available. Standardized assays to assess the immunogenicity of new vaccines are crucial for vaccine development and evaluation of other therapeutics. Therefore, we have developed assays that can assess the functionality of antibodies, which can be used to evaluate the potential of novel K. pneumoniae conjugate vaccines, and inform which antibodies are most effective for preventing disease.

It is estimated that one out of seven health insurance claims is rejected in the US; hospitals across the country lose approximately $262 billion annually due to denied claims. This widespread problem causes huge cash-flow issues and overburdens patients. Thus, preventing claim denials before claims are submitted to insurers improves profitability, accelerates the revenue cycle, and supports patients’ wellbeing. This study utilizes Design Science Research (DSR) paradigm and develops a Responsible Artificial Intelligence (RAI) solution helping hospital administrators identify potentially denied claims. Guided by five principles, this framework utilizes six AI algorithms – classified as white-box and glass-box – and employs cross-validation to tune hyperparameters and determine the best model. The results show that a white-box algorithm (AdaBoost) model yields an AUC rate of 0.83, outperforming all other models. This research’s primary implications are to (1) help providers reduce operational costs and increase the efficiency of insurance claim processes (2) help patients focus on their recovery instead of dealing with appealing claims.