Corrigendum: Application of a multispecies probiotic reduces gastro-intestinal discomfort and induces microbial changes after colonoscopy.

[This corrects the article DOI: 10.3389/fonc.2022.1078315.].

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review.

BACKGROUND: There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities. PURPOSE: To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities. DATA SOURCES: Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023. STUDY SELECTION: Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. DATA EXTRACTION: Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension. DATA SYNTHESIS: Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques. LIMITATION: Results are mostly based on modeling studies and may be highly context-specific. CONCLUSION: Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes. PRIMARY FUNDING SOURCE: Agency for Healthcare Quality and Research.

Transplant recipient, care partner, and clinician perceptions of medication adherence monitoring technology: A mixed methods study.

Medication nonadherence is a leading cause of graft loss. Adherence monitoring technologies-reminder texts, smart bottles, video-observed ingestion, and digestion-activated signaling pills-may support adherence. However, patient, care partner, and clinician perceptions of these tools are not well studied. We conducted qualitative individual semistructured interviews and focus groups among 97 participants at a single center: kidney and liver transplant recipients 2 weeks to 18 months posttransplant, their care partners, and transplant clinicians. We assessed adherence practices, reactions to monitoring technologies, and opportunities for care integration. One-size-fits-all approaches were deemed infeasible. Interviewees considered text messages the most acceptable approach; live video checks were the least acceptable and raised the most concerns for inconvenience and invasiveness. Digestion-activated signaling technology produced both excitement and apprehension. Patients and care partners generally aligned in perceptions of adherence monitoring integration into clinical care. Key themes were importance of routine, ease of use, leveraging technology for actionable medication changes, and aversion to surveillance. Transplant clinicians similarly considered text messages most acceptable and video checks least acceptable. Clinicians reported that early posttransplant use and real-time adherence tracking with patient feedback may facilitate successful implementation. The study provides initial insights that may inform future adherence technology implementation.

Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models Extension: Development of a Critical Appraisal Tool Extension to Assess Racial and Ethnic Equity-Related Risk of Bias for Clinical Prediction Models.

Introduction: Despite mounting evidence that the inclusion of race and ethnicity in clinical prediction models may contribute to health disparities, existing critical appraisal tools do not directly address such equity considerations. Objective: This study developed a critical appraisal tool extension to assess algorithmic bias in clinical prediction models. Methods: A modified e-Delphi approach was utilized to develop and obtain expert consensus on a set of racial and ethnic equity-based signaling questions for appraisal of risk of bias in clinical prediction models. Through a series of virtual meetings, initial pilot application, and an online survey, individuals with expertise in clinical prediction model development, systematic review methodology, and health equity developed and refined this tool. Results: Consensus was reached for ten equity-based signaling questions, which led to the development of the Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models (CARE-CPM) extension. This extension is intended for use along with existing critical appraisal tools for clinical prediction models. Conclusion: CARE-CPM provides a valuable risk-of-bias assessment tool extension for clinical prediction models to identify potential algorithmic bias and health equity concerns. Further research is needed to test usability, interrater reliability, and application to decision-makers.

The Concise Guide to PHARMACOLOGY 2023/24: Catalytic receptors.

The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and nearly 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org/), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16180. Catalytic receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein-coupled receptors, ion channels, nuclear hormone receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.

Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes.

Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-ß (Aß) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.

Analysis of the Diffusion in a Multilayer Structure under a Constant Heating Rate: The Calculation of Activation Energy from the In Situ Neutron Reflectometry Measurement.

Understanding mass transport in micro- and nanostructures is of paramount importance in improving the performance and reliability of the micro- and nanostructures. In this work, we solve the diffusion problem in a multilayer structure with periodic conditions under a constant heating rate via a Fourier series. Analytical relation is established between the coefficients of eigenfunctions and the intensity of X-ray or neutron Bragg peak. The logarithm of temporal variation of the intensity of X-ray or neutron Bragg peak is a linear function of the nominal diffusion time, with the nominal diffusion time being dependent on the heating rate. This linear relation is validated by experimental data. The Taylor series expansion of the linear relation to the first order of the diffusion time yields an approximately linear relation between the logarithm of temporal variation of the intensity of X-ray or neutron peak and the diffusion time for small diffusion times, which can be likely used to calculate the activation energy for the diffusion in a multilayer structure. The validation of such an approach is subjected to the fact that the characteristic time for heat conduction is much less than the characteristic time for the ramp heating as well as the characteristic time for diffusion.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

BACKGROUND: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.

The Supreme Court's Rulings on Race Neutrality Threaten Progress in Medicine and Health.

This Viewpoint examines the recent Supreme Court rulings on race neutrality, striking down affirmative action programs in higher education, which will affect efforts to eliminate health inequities in the US.

What Should Be Roles of Federal Clinician Governors in Motivating Equity in Locally Coordinated Triage Protocols?

This commentary on a case examines racially inequitable outcomes, especially for Black patients, resulting from use of Sequential Organ Failure Assessment (SOFA) scores to triage patients during the COVID-19 pandemic and how inequitable outcomes in triage protocols could be reduced. It also considers the nature and scope of clinician governor responses to members of federally protected classes who are disadvantaged by use of the SOFA score and argues that clinician leaders of the Centers for Disease Control and Prevention, specifically, should provide federal guidance that motivates clear legal accountability.

Using a patient portal as a recruitment tool to diversify the pool of participants in COVID-19 vaccine clinical trials.

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected racial/ethnic minorities in the United States, who are underrepresented in clinical trials. We assessed the feasibility of using the University of Pennsylvania Health System electronic health record patient portal to diversify the pool of participants in COVID-19 vaccine clinical trials. The patient portal was used to send invitations to eligible individuals living in zip codes with high rates of racial/ethnic minorities. The 5614 invited consisted of 96.7% black, 1.3% Hispanic/Latinx, and 1.5% white. The overall response rate was 5.4%, with lower response rates among Black (3.8%) and Hispanic/Latinx (9.6%) as compared to white individuals (91.6%). Among respondents, black individuals had lower rates of interest in participating (26.7%), as compared to white (65.8%) and Hispanic/Latinx (71.4%) individuals. Of 115 respondents who expressed interest, 9 enrolled in the clinical trial, which included 6 black, 3 white, and 1 Hispanic/Latinx. During phone outreach to nonresponders and decliners, common reasons for declining included mistrust of the COVID-19 vaccine, underlying health conditions, and logistical barriers to trial participation. Because of low rates of patient portal account activation and use, compounded with vaccine hesitancy, this method yielded a small number of interested individuals.

Associations of 4 Geographic Social Vulnerability Indices With US COVID-19 Incidence and Mortality.

Objectives. To examine and compare how 4 indices of population-level social disadvantage-the Social Vulnerability Index (SVI), the Area Deprivation Index (ADI), the COVID-19 Community Vulnerability Index (CCVI), and the Minority Health-Social Vulnerability Index (MH-SVI)-are associated with COVID-19 outcomes. Methods. Spatial autoregressive models adjusted for population density, urbanicity, and state fixed effects were used to estimate associations of county-level SVI, MH-SVI, CCVI, and ADI values with COVID-19 incidence and mortality. Results. All 4 disadvantage indices had similar positive associations with COVID-19 incidence. Each index was also significantly associated with COVID-19 mortality, but the ADI had a stronger association than the CCVI, MH-SVI, and SVI. Conclusions. Despite differences in component measures and weighting, all 4 of the indices we assessed demonstrated associations between greater disadvantage and COVID-19 incidence and mortality. Public Health Implications. Our findings suggest that each of the 4 disadvantage indices can be used to assist public health leaders in targeting ongoing first-dose and booster or third-dose vaccines as well as new vaccines or other resources to regions most vulnerable to negative COVID-19 outcomes, weighing potential tradeoffs in their political and practical acceptability. (Am J Public Health. 2022;112(11):1584-1588. https://doi.org/10.2105/AJPH.2022.307018).

Disadvantage Indices Deployed to Promote Equitable Allocation of COVID-19 Vaccines in the US: A Scoping Review of Differences and Similarities in Design.

This scoping review identifies the construction and defined purpose of disadvantage indices deployed during the initial COVID-19 vaccine rollout.

Integrating ART adherence support technologies in the care of pregnant and postpartum people with HIV: a qualitative study.

BACKGROUND: We have a limited understanding on how to best integrate technologies to support antiretroviral therapy (ART) adherence in routine HIV care. METHODS: We conducted semi-structured interviews with multidisciplinary providers caring for pregnant and postpartum people with HIV and asked providers about their perspectives on utilizing adherence support technologies such as text messages, video check-ins with providers or automated with facial recognition for directly-observed-therapy, signaling pill bottle, and signaling pill to support ART adherence. Each approach generated an adherence report. The interview instrument was guided by the Consolidated Framework for Implementation Research and included questions on the implementation climate, barriers, and facilitators to the clinical integration of the adherence approach and strategies that could be used to maximize this integration. The order of adherence support technologies was randomized to minimize bias. We used a modified grounded theory to develop the coding structure and two coders applied the codebook to the transcripts after establishing strong inter-rater reliability with 20% of interviews (kappa = 0.82). RESULTS: Between March and December 2020, we conducted 26 in-depth, semi-structured interviews with providers who weighed several factors when considering each approach, including the approach's effect on patient-provider interaction in and outside of the clinic visit, timing for and duration of the approach's utility, threat of disclosing status, and added burden to providers (e.g., needing to act on generated information) or to patients (e.g., needing to hide the signaling pills, responding to text messages). Providers' most preferred approach was text-messages, and the least preferred was the signaling pill. Barriers to acceptability varied by approach and included perceived surveillance, violation of privacy, added time demand for providers, potential inaccuracy of the adherence data generated, and negative impact on the patient-provider relationship, particularly if the approach was perceived as coercive. Payers anticipated regulatory hurdles with unfamiliar approaches, particularly the signaling pill and signaling pill bottle. Facilitators included strengthened therapeutic alliance, predictable reminder mechanisms, and options for customization according to patient preference. CONCLUSIONS: Our study elucidates barriers and facilitators to integrating technology-based adherence support approaches in clinical care to support adherence of pregnant and postpartum people with HIV.

Public attitudes about equitable COVID-19 vaccine allocation: a randomised experiment of race-based versus novel place-based frames.

Equity was-and is-central in the US policy response to COVID-19, given its disproportionate impact on disadvantaged communities of colour. In an unprecedented turn, the majority of US states used place-based disadvantage indices to promote equity in vaccine allocation (eg, through larger vaccine shares for more disadvantaged areas and people of colour).We conducted a nationally representative survey experiment (n=2003) in April 2021 (before all US residents had become vaccine eligible), that examined respondents' perceptions of the acceptability of disadvantage indices relative to two ways of prioritising racial and ethnic groups more directly, and assessed the role of framing and expert anchors in shaping perceptions.A majority of respondents supported the use of disadvantage indices, and one-fifth opposed any of the three equity-promoting plans. Differences in support and opposition were identified by respondents' political party affiliation. Providing a numerical anchor (that indicated expert recommendations and states' actual practices in reserving a proportion of allocations for prioritised groups) led respondents to prefer a lower distribution of reserved vaccine allocations compared with the randomised condition without this anchor, and the effect of the anchor differed across the frames.Our findings support ongoing uses of disadvantage indices in vaccine allocation, and, by extension, in allocating tests, masks or treatments, especially when supply cannot meet demand. The findings can also inform US allocation frameworks in future pandemic planning, and could provide lessons on how to promote equity in clinical and public health outside of the pandemic setting.

Soluble guanylate cyclase stimulator riociguat improves spatial memory in mice via peripheral mechanisms.

Soluble guanylate cyclase (sGC) - cyclic guanosine monophosphate (cGMP) signalling is important for healthy memory function and a healthy vascular system. Targeting sGC-cGMP signalling can therefore be a potential strategy to enhance memory processes. sGC can be targeted by using agonists, such as sGC stimulator riociguat. Therefore, this study aimed to target sGC using riociguat to investigate its acute effects on memory function and neuronal plasticity in mice. The effects of riociguat on long-term memory and a biperiden-induced memory deficit model for assessing short-term memory were tested in the object location task, and working memory was tested in the Y-maze continuous alternation task. Pharmacokinetic measurements were performed within brain tissue of mice, and hippocampal plasticity measures were assessed using western blotting. Acute oral administration with a low dose of 0.03 mg/kg riociguat was able to enhance working-, short-, and long-term spatial memory. Under cerebral vasoconstriction higher doses of riociguat were still effective on memory. Pharmacokinetic measurements revealed poor brain penetration of riociguat and its metabolite M-1. Increased activation of VASP was found, while no effects were found on other memory-related hippocampal plasticity measures. Memory enhancing effects of riociguat are most likely regulated by vascular peripheral effects on cGMP signalling. Yet, further research is needed to investigate the possible contribution of hemodynamic or metabolic effects of sGC stimulators on memory performance.

Endothelial reactive oxygen-forming NADPH oxidase 5 is a possible player in diabetic aortic aneurysm but not atherosclerosis.

Atherosclerosis and its complications are major causes of cardiovascular morbidity and death. Apart from risk factors such as hypercholesterolemia and inflammation, the causal molecular mechanisms are unknown. One proposed causal mechanism involves elevated levels of reactive oxygen species (ROS). Indeed, early expression of the ROS forming NADPH oxidase type 5 (Nox5) in vascular endothelial cells correlates with atherosclerosis and aortic aneurysm. Here we test the pro-atherogenic Nox5 hypothesis using mouse models. Because Nox5 is missing from the mouse genome, a knock-in mouse model expressing human Nox5 in its physiological location of endothelial cells (eNOX5ki/ki) was tested as a possible new humanised mouse atherosclerosis model. However, whether just on a high cholesterol diet or by crossing in aortic atherosclerosis-prone ApoE-/- mice with and without induction of diabetes, Nox5 neither induced on its own nor aggravated aortic atherosclerosis. Surprisingly, however, diabetic ApoE-/- x eNOX5ki/ki mice developed aortic aneurysms more than twice as often correlating with lower vascular collagens, as assessed by trichrome staining, without changes in inflammatory gene expression, suggesting that endothelial Nox5 directly affects extracellular matrix remodelling associated with aneurysm formation in diabetes. Thus Nox5-derived reactive oxygen species are not a new independent mechanism of atherosclerosis but may enhance the frequency of abdominal aortic aneurysms in the context of diabetes. Together with similar clinical findings, our preclinical target validation opens up a first-in-class mechanism-based approach to treat or even prevent abdominal aortic aneurysms.

Un-biased housekeeping gene panel selection for high-validity gene expression analysis.

Differential gene expression normalised to a single housekeeping (HK) is used to identify disease mechanisms and therapeutic targets. HK gene selection is often arbitrary, potentially introducing systematic error and discordant results. Here we examine these risks in a disease model of brain hypoxia. We first identified the eight most frequently used HK genes through a systematic review. However, we observe that in both ex-vivo and in vivo, their expression levels varied considerably between conditions. When applying these genes to normalise expression levels of the validated stroke target gene, inducible Nox4, we obtained opposing results. As an alternative tool for unbiased HK gene selection, software tools exist but are limited to individual datasets lacking genome-wide search capability and user-friendly interfaces. We, therefore, developed the HouseKeepR algorithm to rapidly analyse multiple gene expression datasets in a disease-specific manner and rank HK gene candidates according to stability in an unbiased manner. Using a panel of de novo top-ranked HK genes for brain hypoxia, but not single genes, Nox4 induction was consistently reproduced. Thus, differential gene expression analysis is best normalised against a HK gene panel selected in an unbiased manner. HouseKeepR is the first user-friendly, bias-free, and broadly applicable tool to automatically propose suitable HK genes in a tissue- and disease-dependent manner.

Tracer diffusion in proton-exchanged congruent LiNbO3 crystals as a function of hydrogen content.

The proton-exchange process is an effective method of fabricating low-loss waveguides based on LiNbO3 crystals. During proton-exchange, lithium is replaced by hydrogen and Li1-xHxNbO3 is formed. Currently, mechanisms and kinetics of the proton-exchange process are unclear, primarily due to a lack in reliable tracer diffusion data. We studied lithium and hydrogen tracer diffusion in proton-exchanged congruent LiNbO3 single crystals in the temperature range between 130-230 °C. Proton-exchange was done in benzoic acid with 0, 1, 2, or 3.6 mol% lithium benzoate added, resulting in micrometre thick surface layers where Li is substituted by H with relative fractions between x = 0.45 and 0.85 as determined by Nuclear Reaction Analysis. For the diffusion experiments, ion-beam sputtered isotope enriched 6LiNbO3 was used as a Li tracer source and deuterated benzoic acid as a H tracer source. Isotope depth profile analysis was carried out by secondary ion mass spectrometry. From the experimental results, effective diffusivities governing the lithium/hydrogen exchange as well as individual hydrogen and lithium tracer diffusivities are extracted. All three types of diffusivities can be described by the Arrhenius law with an activation enthalpy of about 1.0-1.2 eV and increase as a function of hydrogen content nearly independent of temperature. The effective diffusivities and the lithium tracer diffusivities are identical within a factor of two to five, while the hydrogen diffusivities are higher by three orders of magnitude. The results show that the diffusion of Li is the rate determining step governing the proton-exchange process. Exponential dependencies between diffusivities and hydrogen concentrations are determined. The observed increase of Li tracer diffusivities and effective diffusivities as a function of hydrogen concentration is attributed to a continuous reduction of the migration enthalpy of diffusion by a maximum factor of about 0.2 eV. Simulations based on the determined diffusivities can reproduce the step-like profile of hydrogen penetration during proton-exchange.