Fe(II)-Catalyzed Recrystallization Drives Phosphorus and Aluminum Release from Goethite.

Goethite often harbors impurities, such as phosphorus (P) and aluminum (Al), which are incorporated into its structure through direct substitution or coprecipitation with nanocrystalline phases. Understanding the processes that drive the release of P and Al from goethite is of paramount importance for the iron ore industry and for managing nutrient and pollutant behavior in the environment. This study investigates the impact of Fe(II)-catalyzed recrystallization on the release of P and Al from goethite. We evaluated the solubility and extractability of P and Al in suspensions of Al- and P-coprecipitated goethite, treated with 57Fe-enriched Fe(II)aq under oxygen-free conditions for 30 days at neutral pH and room temperatures. The addition of Fe(II)aq induced the recrystallization of goethite dominant initial synthetic phases (i.e., low P- and Al-containing phases) and the transformation of higher P- and/or Al-bearing starting material that was actually a mixture of goethite and minor amounts of lepidocrocite and feroxyhyte. Our results reveal that Fe(II)-catalyzed mineral and structural evolution led to the repartitioning of P and, to a lesser extent, Al throughout the crystal structure, mineral surface, and aqueous solution. Following a 30 day reaction with Fe(II)aq, we extracted approximately 80, 68.8, 73.9, and 83.2% of P from P-only, low, medium, and high P + Al goethite, respectively. Additionally, we observed total Al removals of approximately 17, 27, and 25% from low, medium, and high P + Al goethite, respectively. The results demonstrate that treating both P-only and P + Al goethite with Fe(II) at room temperature, followed by a 24 h extraction using 1 M NaOH, significantly enhances the overall extractability of P and Al, including both aqueous and surface-adsorbed fractions, compared to Fe(II)-free controls. These findings advance our understanding of the recrystallization process and impurity substitution in goethite, offering promising avenues for developing new environmentally friendly methods to extract P and other impurities from goethitic iron ores at lower temperatures.

United States Provider Experiences With Telemedicine for Hepatitis C Treatment: A Nationwide Survey.

Hepatitis C virus (HCV) elimination requires treatment access expansion, especially for underserved populations. Telehealth has the potential to improve HCV treatment access, although data are limited on its incorporation into standard clinical practice. We conducted a cross-sectional, email survey of 598 US HCV treatment providers who had valid email addresses and (1) were located in urban areas and had written ≥ 20 prescriptions for HCV treatment to US Medicare beneficiaries in 2019-2020 or (2) were located in non-urban areas and wrote any HCV prescriptions in 2019-2020. Through email, we notified providers of a self-administered electronic 28-item survey of clinical strategies and attitudes about telemedicine for HCV. We received 86 responses (14% response rate), of which 75 used telemedicine for HCV in 2022. Of those 75, 24% were gastroenterologists/hepatologists, 23% general medicine, 17% infectious diseases and 32% non-physicians. Most (82%) referred patients to commercial laboratories, and 85% had medications delivered directly to patients. Overwhelmingly, respondents (92%) felt that telehealth increases healthcare access, and 76% reported that it promotes or is neutral for treatment completion. Factors believed to be 'extremely' or 'very' important for telehealth use included patient access to technology (86%); patients' internet access (74%); laboratory access (76%); reimbursement for video visits (74%) and audio-only visits (66%). Non-physician licensing and liability statutes were rated 'extremely' or 'very' important by 43% and 44%, respectively. Providers felt that telehealth increases HCV treatment access. Major limitations were technological requirements, reimbursement, and access to ancillary services. These findings support the importance of digital equity and literacy to achieve HCV elimination goals.

Interval life events are an important determinant of heterogeneity in outcomes in a randomised trial: a novel, simple method of assessment.

OBJECTIVE: Although life events are clearly important to health, most of the scientific focus has been on baseline life events that occur prior to a study. Life events that occur after enrolment, that is, interval life events, have had almost no attention. The aim of this analysis of data was to develop a method for measuring interval life events that could be used in clinical trials and other longitudinal studies. DESIGN: Small Changes and Lasting Effects (SCALE) was a 12-month weight-loss randomised controlled trial (RCT). This was an analysis of the SCALE follow-up data. SETTING: Healthcare networks, outpatient clinics and community churches in the South Bronx and Harlem areas of New York City. PARTICIPANTS: Overweight black and Latino adults. This analysis focuses on the 330 of the 405 patients who had >4 weeks of follow-up with at least one perceived stress score (PSS). INTERVENTION: The SCALE RCT was published elsewhere and involved positive affect and self-affirmation to increase behaviour change. OUTCOME: 5% weight loss. FOLLOW-UP: Over 12 months, up to 27 follow-ups were conducted that evaluated interval life events, eating and physical activity behaviour, weight and perceived stress. During these follow-ups, participants were asked two open-ended questions to capture interval life events. The interval life events were qualitatively coded into categories. The interval life events categories were compared with interval monthly measures of perceived stress using the 4-item PSS scale. RESULTS: During the interval follow-ups for the RCT, 70.6% of the 330 patients reported at least one interval life event, which occurred during a median of 15 follow-ups (95% CI: 5 to 24). The median number of interval events was 2 (95% CI: 0 to 8): 30.6% reported their own illness; 22%, death or bereavement; 21.8%, illness in the family and 13.1%, family conflicts. The mean perceived stress score (PSS-4) assessed over the year of follow-up was 3.2±2.7. Mean perceived stress (PSS-4) increased, especially for interval financial events, major conflict with a partner and unemployment, but by less for deaths, family illness and family conflict. Participants with the most interval life events had the greatest increase in interval perceived stress (p<0.0001). Of note, neither high baseline perceived stress (PSS-10 >20) nor baseline depression (Patient Health Questionnaire-9 >10) were associated with higher interval life events (p>0.05); but those with lower social support had more events. However, those with either depression or stress had higher interval stress responses. Most participants had neither baseline nor interval events, and the percentage with both was small so that baseline events did not predict subsequent perceived stress. CONCLUSIONS: This method provides a straightforward method of assessing interval life events, by asking two open-ended questions, that can be coded in a simple categorical framework. Such events can affect outcomes in longitudinal studies and trials in part by increasing perceived stress. This framework moves beyond the events identified as important in the 1950s and recognises that specific life events may have significantly different life impacts in different individuals. TRIAL REGISTERATION NUMBER: NCT01198990; Post-results.

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

United States Provider Experiences with Telemedicine for Hepatitis C Treatment: A Nationwide Survey.

Background: Hepatitis C virus (HCV) elimination requires treatment access expansion, especially for underserved populations. Telehealth has the potential to improve HCV treatment access, although data are limited on its incorporation into standard clinical practice. Methods: We conducted a cross-sectional, e-mail survey of 598 US HCV treatment providers who had valid email addresses and 1) were located in urban areas and had written ≥20 prescriptions for HCV treatment to US Medicare beneficiaries in 2019-20 or 2) were located in non-urban areas and wrote any HCV prescriptions in 2019-20. Through email, we notified providers of a self-administered electronic 28-item survey of clinical strategies and attitudes about telemedicine for HCV. Results: We received 86 responses (14% response rate), of which 75 used telemedicine for HCV in 2022. Of those 75, 24% were gastroenterologists/hepatologists, 23% general medicine, 17% infectious diseases, and 32% non-physicians. Most (82%) referred patients to commercial laboratories, and 85% had medications delivered directly to patients. Overwhelmingly, respondents (92%) felt that telehealth increases healthcare access, and 76% reported that it promotes or is neutral for treatment completion. Factors believed to be "extremely" or "very" important for telehealth use included patient access to technology (86%); patients' internet access (74%); laboratory access (76%); reimbursement for video visits (74%) and audio-only visits (66%). Non-physician licensing and liability statutes were rated "extremely" or "very" important by 43% and 44%, respectively. Conclusions: Providers felt that telehealth increases HCV treatment access. Major limitations were technological requirements, reimbursement, and access to ancillary services. These findings support the importance of digital equity and literacy to achieve HCV elimination goals.

Integrative Analysis of Differentially Expressed Genes in Time-Course Multi-Omics Data with MINT-DE.

Background: Time-course multi-omics experiments have been highly informative for obtaining a comprehensive understanding of the dynamic relationships between molecules in a biological process, especially if the different profiles are obtained from the same samples. A fundamental step in analyzing time-course multi-omics data involves selecting a short list of genes or gene regions ("sites") that warrant further study. Two important criteria for site selection are the magnitude of change and the temporal dynamic consistency. However, existing methods only consider one of these criteria, while neglecting the other. Results: In our study, we propose a framework called MINT-DE (Multi-omics INtegration of Time-course for Diffierential Expression analysis) to address this limitation. MINT-DE is capable of selecting sites based on summarized measures of both aforementioned aspects. We calculate evidence measures assessing the extent of differential expression for each assay and for the dynamical similarity across assays. Then based on the summary of the evidence assessment measures, sites are ranked. To evaluate the performance of MINT-DE, we apply it to analyze a time-course multi-omics dataset of Drosophila development. We compare the selection obtained from MINT-DE with those obtained from other existing methods. The analysis reveal that MINT-DE is able to identify differentially expressed time-course pairs with the highest correlations. Their corresponding genes are significantly enriched for known biological functions, as measured by gene-gene interaction networks and the Gene Ontology enrichment. Conclusions: These findings suggest the effectiveness of MINT-DE in selecting sites that are both differentially expressed within at least one assay and temporally related across assays. This highlights the potential of MINT-DE to identify biologically important sites for downstream analysis and provide a complementarity of sites that is neglected by existing methods.

Hepatitis C Treatment Initiation Among US Medicaid Enrollees.

Importance: Direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection is highly effective but remains underused. Understanding disparities in the delivery of DAAs is important for HCV elimination planning and designing interventions to promote equitable treatment. Objective: To examine variations in the receipt of DAA in the 6 months following a new HCV diagnosis. Design, Setting, and Participants: This retrospective cohort study used national Medicaid claims from 2017 to 2019 from 50 states, Washington DC, and Puerto Rico. Individuals aged 18 to 64 years with a new diagnosis of HCV in 2018 were included. A new diagnosis was defined as a claim for an HCV RNA test followed by an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis code, after a 1-year lookback period. Main Outcomes and Measures: Outcome was receipt of a DAA prescription within 6 months of diagnosis. Logistic regression was used to examine demographic factors and ICD-10-identified comorbidities associated with treatment initiation. Results: Among 87 652 individuals, 43 078 (49%) were females, 12 355 (14%) were age 18 to 29 years, 35 181 (40%) age 30 to 49, 51 282 (46%) were non-Hispanic White, and 48 840 (49%) had an injection drug use diagnosis. Of these individuals, 17 927 (20%) received DAAs within 6 months of their first HCV diagnosis. In the regression analyses, male sex was associated with increased treatment initiation (OR, 1.24; 95% CI, 1.16-1.33). Being age 18 to 29 years (OR, 0.65; 95% CI, 0.50-0.85) and injection drug use (OR, 0.84; 95% CI, 0.75-0.94) were associated with decreased treatment initiation. After adjustment for state fixed effects, Asian race (OR, 0.50; 95% CI, 0.40-0.64), American Indian or Alaska Native race (OR, 0.68; 95% CI, 0.55-0.84), and Hispanic ethnicity (OR, 0.81; 95% CI, 0.71-0.93) were associated with decreased treatment initiation. Adjustment for state Medicaid policy did not attenuate the racial or ethnic disparities. Conclusions: In this retrospective cohort study, HCV treatment initiation was low among Medicaid beneficiaries and varied by demographic characteristics and comorbidities. Interventions are needed to increase HCV treatment uptake among Medicaid beneficiaries and to address disparities in treatment among key populations, including younger individuals, females, individuals from minoritized racial and ethnic groups, and people who inject drugs.

Two-sample inference procedures under nonproportional hazards.

We introduce a new two-sample inference procedure to assess the relative performance of two groups over time. Our model-free method does not assume proportional hazards, making it suitable for scenarios where nonproportional hazards may exist. Our procedure includes a diagnostic tau plot to identify changes in hazard timing and a formal inference procedure. The tau-based measures we develop are clinically meaningful and provide interpretable estimands to summarize the treatment effect over time. Our proposed statistic is a U-statistic and exhibits a martingale structure, allowing us to construct confidence intervals and perform hypothesis testing. Our approach is robust with respect to the censoring distribution. We also demonstrate how our method can be applied for sensitivity analysis in scenarios with missing tail information due to insufficient follow-up. Without censoring, Kendall's tau estimator we propose reduces to the Wilcoxon-Mann-Whitney statistic. We evaluate our method using simulations to compare its performance with the restricted mean survival time and log-rank statistics. We also apply our approach to data from several published oncology clinical trials where nonproportional hazards may exist.

On Graphical Models and Convex Geometry.

A mixture-model of beta distributions framework is introduced to identify significant correlations among P features when P is large. The method relies on theorems in convex geometry, which are used to show how to control the error rate of edge detection in graphical models. The proposed 'betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results hold for a wide class of data-generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions. The results are robust for sufficiently large sample sizes and hold for non-elliptically-symmetric distributions.

Time series transcriptome analysis implicates the circadian clock in the Drosophila melanogaster female's response to sex peptide.

Sex peptide (SP), a seminal fluid protein of Drosophila melanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses including increased egg laying, activity, and food intake and a decreased remating rate. While it is known that SP achieves this, at least in part, by altering neuronal signaling in females, the genetic architecture and temporal dynamics of the female's response to SP remain elusive. We used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 h after mating to learn about the genetic architecture, at the gene and exon levels, of the female's response to SP. We find that SP is not essential to trigger early aspects of a virgin-to-mated transcriptional switch, which includes changes in a metabolic gene regulatory network. However, SP is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that SP alters rhythmic gene expression in females and suggests that SP's disruption of the female's circadian rhythm might be key to its widespread effects.

Assessing the robustness of results from clinical trials and meta-analyses with the fragility index.

The fragility index has been increasingly used to assess the robustness of the results of clinical trials since 2014. It aims at finding the smallest number of event changes that could alter originally statistically significant results. Despite its popularity, some researchers have expressed several concerns about the validity and usefulness of the fragility index. It offers a comprehensive review of the fragility index's rationale, calculation, software, and interpretation, with emphasis on application to studies in obstetrics and gynecology. This article presents the fragility index in the settings of individual clinical trials, standard pairwise meta-analyses, and network meta-analyses. Moreover, this article provides worked examples to demonstrate how the fragility index can be appropriately calculated and interpreted. In addition, the limitations of the traditional fragility index and some solutions proposed in the literature to address these limitations were reviewed. In summary, the fragility index is recommended to be used as a supplemental measure in the reporting of clinical trials and a tool to communicate the robustness of trial results to clinicians. Other considerations that can aid in the fragility index's interpretation include the loss to follow-up and the likelihood of data modifications that achieve the loss of statistical significance.

Towards a single-assay approach: a combined DNA/RNA sequencing panel eliminates diagnostic redundancy and detects clinically-relevant fusions in neuropathology.

Since the introduction of integrated histological and molecular diagnoses by the 2016 World Health Organization (WHO) Classification of Tumors of the Nervous System, an increasing number of molecular markers have been found to have prognostic significance in infiltrating gliomas, many of which have now become incorporated as diagnostic criteria in the 2021 WHO Classification. This has increased the applicability of targeted-next generation sequencing in the diagnostic work-up of neuropathology specimens and in addition, raises the question of whether targeted sequencing can, in practice, reliably replace older, more traditional diagnostic methods such as immunohistochemistry and fluorescence in-situ hybridization. Here, we demonstrate that the Oncomine Cancer Gene Mutation Panel v2 assay targeted-next generation sequencing panel for solid tumors is not only superior to IHC in detecting mutation in IDH1/2 and TP53 but can also predict 1p/19q co-deletion with high sensitivity and specificity relative to fluorescence in-situ hybridization by looking at average copy number of genes sequenced on 1p, 1q, 19p, and 19q. Along with detecting the same molecular data obtained from older methods, targeted-next generation sequencing with an RNA sequencing component provides additional information regarding the presence of RNA based alterations that have diagnostic significance and possible therapeutic implications. From this work, we advocate for expanded use of targeted-next generation sequencing over more traditional methods for the detection of important molecular alterations as a part of the standard diagnostic work up for CNS neoplasms.

Assessing mortality differences across acute respiratory failure management strategies in Covid-19.

PURPOSE: Prolonged observation could avoid invasive mechanical ventilation (IMV) and related risks in patients with Covid-19 acute respiratory failure (ARF) compared to initiating early IMV. We aimed to determine the association between ARF management strategy and in-hospital mortality. MATERIALS AND METHODS: Patients in the Weill Cornell Covid-19 registry who developed ARF between March 5 - March 25, 2020 were exposed to an early IMV strategy; between March 26 - April 1, 2020 to an intermediate strategy; and after April 2 to prolonged observation. Cox proportional hazards regression was used to model in-hospital mortality and test an interaction between ARF management strategy and modified sequential organ failure assessment (mSOFA). RESULTS: Among 632 patients with ARF, 24% of patients in the early IMV strategy died versus 28% in prolonged observation. At lower mSOFA, prolonged observation was associated with lower mortality compared to early IMV (at mSOFA = 0, HR 0.16 [95% CI 0.04-0.57]). Mortality risk increased in the prolonged observation strategy group with each point increase in mSOFA score (HR 1.29 [95% CI 1.10-1.51], p = 0.002). CONCLUSION: In Covid-19 ARF, prolonged observation was associated with a mortality benefit at lower mSOFA scores, and increased mortality at higher mSOFA scores compared to early IMV.

Collective effects of human genomic variation on microbiome function.

Studies of the impact of host genetics on gut microbiome composition have mainly focused on the impact of individual single nucleotide polymorphisms (SNPs) on gut microbiome composition, without considering their collective impact or the specific functions of the microbiome. To assess the aggregate role of human genetics on the gut microbiome composition and function, we apply sparse canonical correlation analysis (sCCA), a flexible, multivariate data integration method. A critical attribute of metagenome data is its sparsity, and here we propose application of a Tweedie distribution to accommodate this. We use the TwinsUK cohort to analyze the gut microbiomes and human variants of 250 individuals. Sparse CCA, or sCCA, identified SNPs in microbiome-associated metabolic traits (BMI, blood pressure) and microbiome-associated disorders (type 2 diabetes, some neurological disorders) and certain cancers. Both common and rare microbial functions such as secretion system proteins or antibiotic resistance were found to be associated with host genetics. sCCA applied to microbial species abundances found known associations such as Bifidobacteria species, as well as novel associations. Despite our small sample size, our method can identify not only previously known associations, but novel ones as well. Overall, we present a new and flexible framework for examining host-microbiome genetic interactions, and we provide a new dimension to the current debate around the role of human genetics on the gut microbiome.

Predictors of life-threatening complications in relatively lower-risk patients hospitalized with COVID-19.

Older individuals with chronic health conditions are at highest risk of adverse clinical outcomes from COVID-19, but there is widespread belief that risk to younger, relatively lower-risk individuals is negligible. We assessed the rate and predictors of life-threatening complications among relatively lower-risk adults hospitalized with COVID-19. Of 3766 adults hospitalized with COVID-19 to three hospitals in New York City from March to May 2020, 963 were relatively lower-risk based on absence of preexisting health conditions. Multivariable logistic regression models examined in-hospital development of life-threatening complications (major medical events, intubation, or death). Covariates included age, sex, race/ethnicity, hypertension, weight, insurance type, and area-level sociodemographic factors (poverty, crowdedness, and limited English proficiency). In individuals ≥55 years old (n = 522), 33.3% experienced a life-threatening complication, 17.4% were intubated, and 22.6% died. Among those <55 years (n = 441), 15.0% experienced a life-threatening complication, 11.1% were intubated, and 5.9% died. In multivariable analyses among those ≥55 years, age (OR 1.03 [95%CI 1.01-1.06]), male sex (OR 1.72 [95%CI 1.14-2.64]), being publicly insured (versus commercial insurance: Medicare, OR 2.02 [95%CI 1.22-3.38], Medicaid, OR 1.87 [95%CI 1.10-3.20]) and living in areas with relatively high limited English proficiency (highest versus lowest quartile: OR 3.50 [95%CI 1.74-7.13]) predicted life-threatening complications. In those <55 years, no sociodemographic factors significantly predicted life-threatening complications. A substantial proportion of relatively lower-risk patients hospitalized with COVID-19 experienced life-threatening complications and more than 1 in 20 died. Public messaging needs to effectively convey that relatively lower-risk individuals are still at risk of serious complications.

Fragility indices for only sufficiently likely modifications.

The fragility index is a clinically meaningful metric based on modifying patient outcomes that is increasingly used to interpret the robustness of clinical trial results. The fragility index relies on a concept that explores alternative realizations of the same clinical trial by modifying patient measurements. In this article, we propose to generalize the fragility index to a family of fragility indices called the incidence fragility indices that permit only outcome modifications that are sufficiently likely and provide an exact algorithm to calculate the incidence fragility indices. Additionally, we introduce a far-reaching generalization of the fragility index to any data type and explain how to permit only sufficiently likely modifications for nondichotomous outcomes. All of the proposed methodologies follow the fragility index concept.