Racial and Ethnic Disparities in Characteristics and Care Patterns of Chronic Hepatitis B Patients in the United States.

BACKGROUND & AIMS: Chronic hepatitis B (CHB) disproportionately impacts foreign-born patients and those of Asian or Black race. Given the paucity of data, we aimed to study the impact of race and ethnicity on CHB patient characteristics and management. METHODS: A retrospective analysis of adult CHB patients using data recorded in the deidentified Optum Clinformatics Data Mart Database (January 2003‒March 2021) was performed. We characterized and examined the rates of receiving adequate treatment evaluation (measuring hepatitis B virus DNA and alanine transaminase) and hepatitis B virus treatment among the racial and ethnic groups. RESULTS: The study cohort included 42,140 patients: age, 51.9 ± 15.1 years; 56.1% male; 47% Asian; 26% White; 11% Black; and 7% Hispanic. Thirty-three percent of White and 48% of Asian patients had an annual household income greater than $100,000 US compared with 16% for Black and 25% for Hispanic patients (P < .001), with similar disparities in educational levels. Approximately one third of White (29.3%), Black (35.1%), and Hispanic (35.4%), and half of Asian (49.9%) patients received adequate evaluation (P < .001). Among patients who met American Association for the Study of Liver Diseases treatment criteria, treatment rates were similar among White (60.8%; P = .09) and Black (62.8%; P = .48), but lower among Hispanic (54.7%; P = .03), as compared with Asian patients (65.4%). On multivariable logistic regression adjusted for age, sex, provider type, viral co-infection, and fatty liver disease, Hispanic patients were less likely to receive treatment (adjusted hazard ratio, 0.69; 95% CI, 0.53‒0.91; P = .01) compared with Asian patients. CONCLUSIONS: Compared with Asian CHB patients, non-Asian patients were less likely to undergo adequate evaluation and Hispanic patients were less likely to receive treatment for CHB. Additional efforts are needed to improve CHB management, especially for non-Asian patients.

Renal Outcomes With Tenofovir Alafenamide in Liver Transplant Recipients.

Tenofovir disoproxil fumarate (TDF) is associated with a higher risk of nephrotoxicity compared with entecavir (ETV) or tenofovir alafenamide (TAF).1,2 One-fifth of transplant recipients develop chronic kidney disease (CKD) within 5 years after transplantation, contributed by the use of nephrotoxic immunosuppressive medications.3 Prior studies conducted in the nontransplant setting reported superior renal safety in TAF compared with TDF but data in liver transplant (LT) recipients have so far been limited to small case series.1,4-6 Therefore, the goals of this study were to examine changes in renal function in a large multicenter cohort of LT recipients with chronic hepatitis B who were treated with TAF, TDF, or ETV for the prevention of hepatitis B virus (HBV) reinfection or reactivation from receipt of a positive HBV core antibody graft.

Treatment and Renal Outcomes Up to 96 Weeks After Tenofovir Alafenamide Switch From Tenofovir Disoproxil Fumarate in Routine Practice.

BACKGROUND AND AIMS: Real-world data for treatment effectiveness and renal outcomes in chronic hepatitis B (CHB) patients who were switched to the new and safer prodrug tenofovir alafenamide (TAF) from tenofovir disoproxil fumarate (TDF) are limited. Therefore, we aimed to evaluate treatment and renal outcomes of this population. APPROACH AND RESULTS: We analyzed 834 patients with CHB previously treated with TDF for ≥12 months who were switched to TAF in routine practice at 13 US and Asian centers for changes in viral (HBV DNA < 20 IU/mL), biochemical (alanine aminotransferase [ALT] < 35/25 U/L for male/female), and complete (viral+biochemical) responses, as well as estimated glomerular filtration rate (eGFR; milliliters per minute per 1.73 square meters) up to 96 weeks after switch. Viral suppression (P < 0.001) and ALT normalization (P = 0.003) rates increased significantly after switch, with a trend for increasing complete response (Ptrend = 0.004), while the eGFR trend (Ptrend  > 0.44) or mean eGFR (P > 0.83, adjusted for age, sex, baseline eGFR, and diabetes, hypertension, or cirrhosis by generalized linear modeling) remained stable. However, among those with baseline eGFR < 90 (chronic kidney disease [CKD] stage ≥2), mean eGFR decreased significantly while on TDF (P = 0.029) but not after TAF switch (P = 0.90). By week 96, 21% (55/267) of patients with CKD stage 2 at switch improved to stage 1 and 35% (30/85) of CKD stage 3-5 patients improved to stage 2 and 1.2% (1/85) to stage 1. CONCLUSIONS: Overall, we observed continued improvement in virologic response, ALT normalization, and no significant changes in eGFR following switch to TAF from TDF.

Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen.

The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas (a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.

COBRAme: A computational framework for genome-scale models of metabolism and gene expression.

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in iJL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.

Networks of energetic and metabolic interactions define dynamics in microbial communities.

Microorganisms form diverse communities that have a profound impact on the environment and human health. Recent technological advances have enabled elucidation of community diversity at high resolution. Investigation of microbial communities has revealed that they often contain multiple members with complementing and seemingly redundant metabolic capabilities. An understanding of the communal impacts of redundant metabolic capabilities is currently lacking; specifically, it is not known whether metabolic redundancy will foster competition or motivate cooperation. By investigating methanogenic populations, we identified the multidimensional interspecies interactions that define composition and dynamics within syntrophic communities that play a key role in the global carbon cycle. Species-specific genomes were extracted from metagenomic data using differential coverage binning. We used metabolic modeling leveraging metatranscriptomic information to reveal and quantify a complex intertwined system of syntrophic relationships. Our results show that amino acid auxotrophies create additional interdependencies that define community composition and control carbon and energy flux through the system while simultaneously contributing to overall community robustness. Strategic use of antimicrobials further reinforces this intricate interspecies network. Collectively, our study reveals the multidimensional interactions in syntrophic communities that promote high species richness and bolster community stability during environmental perturbations.

The changing role of health-oriented international organizations and nongovernmental organizations.

Apart from governments, there are many other actors active in the health policy arena, including a wide array of international organizations (IOs), public-private partnerships and non-governmental organizations (NGOs) that state as their main mission to improve the health of (low-income) populations of low-income countries. Despite the steady rise in numbers and prominence of NGOs, however, there is lack of empirical knowledge about their functioning in the international policy arena, and most studies focus on the larger organizations. This has also caused a somewhat narrow focus of theoretical studies. Some scholars applied the 'principal-agent' theory to study the origins of IOs, for example, other focus on changing power relations. Most of those studies implicitly assume that IOs, public-private partnerships and large NGOs act as unified and rational actors, ignoring internal fragmentation and external pressure to change directions. We assert that the classic analytical instruments for understanding the shaping and outcome of public policy: ideas, interests and institutions apply well to the study of IOs. As we will show, changing ideas about the proper role of state and non-state actors, changing positions and activities of major stakeholders in the (international) health policy arena, and shifts in political institutions that channel the voice of diverging interests resulted in (and reflected) the changing positions of the health-oriented organizations-and also affect their future outlook. Copyright © 2015 John Wiley & Sons, Ltd.

Antiviral Therapy Utilization and 10-Year Outcomes in Resected Hepatitis B Virus- and Hepatitis C Virus-Related Hepatocellular Carcinoma.

PURPOSE: There are limited data on antiviral treatment utilization and its impact on long-term outcomes of hepatitis B virus (HBV)- and hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC) after hepatic resection. We aimed to determine the utilization and impact of antivirals in HBV- and HCV-related HCC. METHODS: This cohort study included 1,906 participants (1,054 HBV-related HCC and 852 HCV-related HCC) from 12 international sites. All participants had HBV- or HCV-related HCC and underwent curative surgical resection. The primary outcome was the utilization of antiviral therapy, and the secondary outcome was long-term overall survival (OS). RESULTS: The mean (±standard deviation [SD]) age was 62.1 (±11.3) years, 74% were male, and 84% were Asian. A total of 47% of the total cohort received antiviral therapy during a mean (±SD) follow-up of 5.0 (±4.3) years. The overall antiviral utilization for participants with HBV-related HCC was 57% and declined over time, from 65% before 2010, to 60% from 2010 to 2015, to 47% beyond 2015, P < .0001. The overall utilization of antivirals for HCV-related HCC was 35% and increased over time, from 24% before 2015 to 74% from 2015 and beyond, P < .0001. The 10-year OS was lower in untreated participants for both HBV (58% v 61%) and HCV participants (38% v 82%; both P < .0001). On multivariable Cox regression analysis adjusted for relevant confounders, antiviral therapy initiated before or within 6 months of HCC diagnosis was independently associated with lower mortality in both HBV- (adjusted hazard ratio [aHR], 0.60 [95% CI, 0.43 to 0.83]; P = .002) and HCV-related HCC (aHR, 0.18 [95% CI, 0.11 to 0.31]; P < .0001). CONCLUSION: Antiviral therapy is associated with long-term survival in people with HBV- or HCV-related HCC who undergo curative resection but is severely underutilized.

Racial and ethnic disparities in untreated patients with hepatitis C virus-related hepatocellular carcinoma but not in those with sustained virologic response.

BACKGROUND: Racial and ethnic disparities exist for hepatitis C virus (HCV) treatment and hepatocellular carcinoma (HCC) survival. AIM: To evaluate the impact of HCV treatment on such disparities. METHODS: In a retrospective cohort study, we analysed 6069 patients with HCV-related HCC (54.2% Asian, 30.1% White, 8.5% Black, and 7.3% Hispanic) from centres in the United States and Asia. RESULTS: The mean age was 61, 60, 59 and 68, respectively, for White, Black, Hispanic and Asian patients. Black patients were most likely to have Barcelona Clinic Liver Cancer stage D, vascular invasion and distant metastasis (23% vs. 5%-15%, 20% vs. 10%-17% and 10% vs. 5%-7%, respectively; all p < 0.0001). Treatment rate with direct-acting antiviral agents (DAA) was 35.9% for Asian, 34.9% for White, 30.3% for Hispanic (30.3%), and 18.7% for Black patients (p < 0.0001). Among those untreated or without sustained virologic response (SVR), 10-year survival rates were 35.4, 27.5, 19.3 and 14.0, respectively, for Asian, Hispanic, White and Black patients (p < 0.0001). There were no statistically significant differences among those with SVR (p = 0.44). On multivariable analysis adjusted for relevant confounders, there was no statistically significant association between survival and being Hispanic (aHR: 0.68, p = 0.26) or Black (aHR: 1.18, p = 0.60) versus White. There was a significant association between being Asian American and survival (aHR: 0.24, p = 0.001; non-U.S. Asian: aHR: 0.66, p = 0.05), and for SVR (aHR: 0.30, p < 0.0001). CONCLUSION: DAA treatment rates were suboptimal. Racial and ethnic disparities resolved with HCV cure. Early diagnosis and improved access to HCV treatment is needed for all patients with HCV infection.

A simple agglutination system for rapid antigen detection from large sample volumes with enhanced sensitivity.

Lateral flow assays (LFAs) provide a simple and quick option for diagnosis and are widely adopted for point-of-care or at-home tests. However, their sensitivity is often limited. Most LFAs only allow 50 µL samples while various sample types such as saliva could be collected in much larger volumes. Adapting LFAs to accommodate larger sample volumes can improve assay sensitivity by increasing the number of target analytes available for detection. Here, a simple agglutination system comprising biotinylated antibody (Ab) and streptavidin (SA) is presented. The Ab and SA agglutinate into large aggregates due to multiple biotins per Ab and multiple biotin binding sites per SA. Dynamic light scattering (DLS) measurements showed that the agglutinated aggregate could reach a diameter of over 0.5 µm and over 1.5 µm using poly-SA. Through both experiments and Monte Carlo modeling, we found that high valency and equivalent concentrations of the two aggregating components were critical for successful agglutination. The simple agglutination system enables antigen capture from large sample volumes with biotinylated Ab and a swift transition into aggregates that can be collected via filtration. Combining the agglutination system with conventional immunoassays, an agglutination assay is proposed that enables antigen detection from large sample volumes using an in-house 3D-printed device. As a proof-of-concept, we developed an agglutination assay targeting SARS-CoV-2 nucleocapsid antigen for COVID-19 diagnosis from saliva. The assay showed a 10-fold sensitivity enhancement when increasing sample volume from 50 µL to 2 mL, with a final limit of detection (LoD) of 10 pg mL-1 (∼250 fM). The assay was further validated in negative saliva spiked with gamma-irradiated SARS-CoV-2 and showed an LoD of 250 genome copies per µL. The proposed agglutination assay can be easily developed from existing LFAs to facilitate the processing of large sample volumes for improved sensitivity.

From private incentives to public health need: rethinking research and development for pandemic preparedness.

Pandemic preparedness and response have relied primarily on market dynamics to drive development and availability of new health products. Building on calls for transformation, we propose a new value proposition that instead prioritises equity from the research and development (R&D) stage and that strengthens capacity to control outbreaks when and where they occur. Key elements include regional R&D hubs free to adapt well established technology platforms, and independent clinical trials networks working with researchers, regulators, and health authorities to better study questions of comparative benefit and real-world efficacy. Realising these changes requires a shift in emphasis: from pandemic response to outbreak control, from one-size-fits-all economies of scale to R&D and manufacture for local need, from de novo product development to last-mile innovation through adaptation of existing technologies, and from proprietary, competitive R&D to open science and financing for the common good that supports collective management and sharing of technology and know-how.