Multi-omics reveal the gut microbiota-mediated severe foraging environment adaption of small wild ruminants in the Three-River-Source National Park, China.

The Tibetan antelope (Pantholops hodgsonii), blue sheep (Pseudois nayaur), and Tibetan sheep (Ovis aries) are the dominant small ruminants in the Three-River-Source National Park (TRSNP). However, knowledge about the association between gut microbiota and host adaptability remains poorly understood. Herein, multi-omics sequencing approaches were employed to investigate the gut microbiota-mediated forage adaption in these ruminants. The results revealed that although wild ruminants (WR) of P. hodgsoni and P. nayaur were faced with severe foraging environments with significantly low vegetation coverage and nutrition, the apparent forage digestibility of dry matter, crude protein, and acid detergent fiber was significantly higher than that of O. aries. The 16s rRNA sequencing showed that the gut microbiota in WR underwent convergent evolution, and alpha diversity in these two groups was significantly higher than that in O. aries. Moreover, indicator species, including Bacteroidetes and Firmicutes, exhibited positive relationships with apparent forage digestibility, and their relative abundances were enriched in the gut of WR. Enterotype analysis further revealed that enterotype 1 belonged to WR, and the abundance of fatty acid synthesis metabolic pathway-related enzyme genes was significantly higher than enterotype 2, represented by O. aries. Besides, the metagenomic analysis identified 14 pathogenic bacterial species, among which 10 potentially pathogenic bacteria were significantly enriched in the gut microbiota of O. aries. Furthermore, the cellulolytic strains and genes encoding cellulase and hemicellulase were significantly enriched in WR. In conclusion, our results provide new evidence of gut microbiota to facilitate wildlife adaption in severe foraging environments of the TRSNP, China.

Aspirin Metabolites and Mammographic Breast Density in Premenopausal Women.

BACKGROUND: Studies investigating the associations of self-reported aspirin use and mammographic breast density (MBD) have reported conflicting results. We, therefore, investigated the associations of aspirin metabolites, with MBD in premenopausal women. METHODS: We performed this study on 705 premenopausal women who had fasting blood draw for metabolomic profiling. We performed covariate-adjusted linear regression models to calculate the least squares means of volumetric measures of MBD (volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)) by quartiles of aspirin metabolites (salicyluric glucuronide, 2-hydroxyhippurate (salicylurate), salicylate, and 2,6-dihydroxybenzoic acid). RESULTS: Approximately 13% of participants reported taking aspirin in the past 12 months. Aspirin users had higher levels of 2-hydroxyhippurate (salicylurate), salicylate, and salicyluric glucuronide (peak area) than non-users, but only mean peak area of salicyluric glucuronide increased by both dose (1-2 tabs per day=1,140,663.7, and ≥3 tabs per day=1,380,476.0) and frequency (days per week: 1 day=888,129.3, 2-3 days=1,199,897.9 and ≥4 days=1,654,637.0). Aspirin metabolites were not monotonically associated with VPD, DV, or NDV. CONCLUSIONS: Given the null results, additional research investigating the associations of aspirin metabolites in breast tissue and MBD is necessary. IMPACT: Elucidating the determinants of MBD, a strong risk factor for breast cancer, can play an important role in breast cancer prevention. Future studies should determine the associations of non-aspirin non-steroidal anti-inflammatory drug metabolites with MBD.

Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework.

OBJECTIVES: Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients' privacy. MATERIALS AND METHODS: We developed a federated learning framework, named dGEM-disparity (decentralized algorithm for Generalized linear mixed Effect Model for disparity quantification). Consisting of 2 modules, dGEM-disparity first provides accurately estimated common effects and calibrated hospital-specific effects by requiring only aggregated data from each center and then adopts a counterfactual modeling approach to assess whether the graft failure rates differ if NHB patients had been admitted at transplant centers in the same distribution as NHW patients were admitted. RESULTS: Utilizing United States Renal Data System data from 39 043 adult patients across 73 transplant centers over 10 years, we found that if NHB patients had followed the distribution of NHW patients in admissions, there would be 38 fewer deaths or graft failures per 10 000 NHB patients (95% CI, 35-40) within 1 year of receiving a kidney transplant on average. DISCUSSION: The proposed framework facilitates efficient collaborations in clinical research networks. Additionally, the framework, by using counterfactual modeling to calculate the event rate, allows us to investigate contributions to racial disparities that may occur at the level of site of care. CONCLUSIONS: Our framework is broadly applicable to other decentralized datasets and disparities research related to differential access to care. Ultimately, our proposed framework will advance equity in human health by identifying and addressing hospital-level racial disparities.

Presence of Epstein-Barr virus in cerebrospinal fluid is associated with increased mortality in HIV-negative cryptococcal meningitis.

Cryptococcus neoformans is the most common cause of fungal meningitis and is associated with a high mortality. The clinical significance of concurrent Epstein-Barr virus (EBV) in the cerebrospinal fluid (CSF) of human immunodeficiency virus (HIV)-negative patients with cryptococcal meningitis (CM) remains unclear. A retrospective cohort study was performed by analyzing CSF samples from 79 HIV-negative Chinese Han patients with confirmed CM. We identified CSF viral DNA in these patients by metagenomic next-generation sequencing (mNGS) and compared 10-week survival rates among those with and without EBV DNA in CSF. Of the 79 CSF samples tested, 44.3% (35/79) had detectable viral DNA in CSF, while 55.7% (44/79) were virus-negative. The most frequent viral pathogen was EBV, which was detected in 22.8% (18/79) patients. The median number of CSF-EBV DNA reads was 4 reads with a range from 1 to 149 reads. The 10-week mortality rates were 22.2% (4/18) in those with positive CSF-EBV and 2.3% (1/44) in those with negative CSF-virus (hazard ratio 8.20, 95% confidence interval [CI] 1.52-81.80; P = 0.014), which remained significant after a multivariate adjustment for the known risk factors of mortality (adjusted hazard ratio 8.15, 95% CI 1.14-92.87; P = 0.037). mNGS can identify viruses that coexist in CSF of HIV-negative patients with CM. EBV DNA is most commonly found together with C. neoformans in CSF and its presence is associated with increased mortality in HIV-negative CM patients.

We retrospectively analyzed CSF samples from 79 HIV-negative Chinese Han patients with confirmed CM. We identified CSF viral DNA by mNGS and compared 10-week survival rates among those with and without EBV DNA. Positive CSF-EBV DNA is associated with the increased mortality in HIV-negative CM patients.

TOpCLASS Expert Consensus Classification of Perianal Fistulizing Crohn's Disease: A Real-World Application in a Serial Fistula MRI Cohort.

BACKGROUND AND AIMS: Perianal fistulizing Crohn's disease (PFCD) is an aggressive phenotype of Crohn's disease defined by frequent relapses and disabling symptoms. A novel consensus classification system was recently outlined by the TOpCLASS consortium that seeks to unify disease severity with patient-centered goals but has not yet been validated. We aimed to apply this to a real-world cohort and identify factors that predict transition between classes over time. METHODS: We identified all patients with PFCD and at least one baseline and one follow-up pelvic (pMRI). TOpCLASS classification, disease characteristics, and imaging indices were collected retrospectively at time periods corresponding with respective MRIs. RESULTS: We identified 100 patients with PFCD of which 96 were assigned TOpCLASS Classes 1 - 2c at baseline. Most patients (78.1%) started in Class 2b, but changes in classification were observed in 52.1% of all patients. Male sex (72.0%, 46.6%, 40.0%, p = 0.03) and prior perianal surgery (52.0% vs 44.6% vs 40.0%, p = 0.02) were more frequently observed in those with improved class. Baseline pMRI indices were not associated with changes in classification, however, greater improvements in mVAI, MODIFI-CD, and PEMPAC were seen among those who improved. Linear mixed effect modeling identified only male sex (-0.31, 95% CI -0.60 to -0.02) with improvement in class. CONCLUSION: The TOpCLASS classification highlights the dynamic nature of PFCD over time, however, our ability to predict transitions between classes remains limited and requires prospective assessment. Improvement in MRI index scores over time was associated with a transition to lower TOpCLASS classification.

Diagnostic efficacy and clinical impact of image-guided core needle biopsy of suspected vertebral osteomyelitis.

OBJECTIVES: The diagnostic yield and clinical impact of image-guided core needle biopsy (ICNB) of suspected vertebral osteomyelitis in adults is heterogenous in published studies owing to small sample sizes, indicating the need for large cohort studies. METHODS: A retrospective analysis of ICNBs was performed from 2010 to 2021 for patients with imaging findings consistent with vertebral osteomyelitis. For each biopsy, a series of factors were analyzed, as well as if histopathology was diagnostic of osteomyelitis and if microbiological cultures were positive. In addition, it was recorded in what way biopsy influenced clinical management regarding antimicrobial treatment. A multivariate statistical analysis was performed to evaluate the factors associated with yield. RESULTS: A total of 570 biopsies performed on 527 patients were included. A histopathologic diagnosis of osteomyelitis was made in 68.4% (359 of 525) of biopsies, and microbiological cultures were positive in 29.6% (169 of 570). Elevated erythrocyte sedimentation rate was positively associated with a histopathologic diagnosis of osteomyelitis (odds ratio [OR] =1.96, P = 0.007) and positive cultures from bone cores (OR = 1.02, P ≤0.001) and aspirate (OR = 1.02, P ≤0.001). Increased total core length was positively associated with a histopathologic diagnosis of osteomyelitis (OR = 1.81, P = 0.013) and positive cultures from bone cores (OR = 1.65, P = 0.049). Clinical management was affected by ICNB in 37.5% (214 of 570) of cases. CONCLUSIONS: In this large cohort, ICNB yielded approximately 30% positive cultures and changed clinical management in over one-third of the patients.

Learning competing risks across multiple hospitals: one-shot distributed algorithms.

OBJECTIVES: To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents. MATERIALS AND METHODS: Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data. RESULTS: The meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data. DISCUSSION: Our proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions. CONCLUSION: Our study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework.

The discrimination between autoimmune glial fibrillary acidic protein astrocytopathy and tuberculous meningitis.

OBJECTIVE: The differential diagnosis between autoimmune glial fibrillary acidic protein astrocytopathy (AGFAPA) mimicking tuberculous meningitis and tuberculous meningitis (TBM) remains challenging in clinical practice. This study aims to identify the clinical, laboratory parameters, and clinical score systems that may be helpful in differentiating AGFAPA from TBM. METHOD: Overall 22 AGFAPA patients who were initially misdiagnosed as TBM (AGFAPA-TBM) and 30 confirmed TBM patients were included. The clinical, laboratory, imaging parameters, Thwaites systems, and Lancet consensus scoring systems (LCSS) of all patients were reviewed. Logistic regression was employed to establish a diagnostic formula to differentiate AGFAPA-TBM from TBM. The receiver operating characteristic (ROC) curve was applied to determine the best diagnostic critical point of the formula. RESULTS: Urinary retention was more frequent in AGFAPA-TBM patients (72.7% vs 33.3%, p = 0.012). A significantly lower ratio of T-SPOT. TB was noted in AGFAPA-TBM patients (9.1% vs 82.1%, p < 0.001). We found the LCSS was able to differentiate AGFAPA-TBM from TBM (AUC value 0.918, 95% CI=0.897-0.924). Furthermore, we set up a new scoring system with three variables: urinary retention, T-SPOT. TB, and cerebral imaging criteria in LCSS. The proposed diagnostic score ranges from -8 to 2, and a score of ≥ 0 was suggestive of AGFAPA-TBM (AUC value 0.938, 95% CI=0.878-0.951). CONCLUSIONS: This study is the first to evaluate the Thwaites system and LCSS in AGFAPA-TBM and TBM. We provide an alternative diagnostic formula to differentiate AGFAPA-TBM from TBM and suggest testing for GFAP antibodies to avoid misdiagnosis when this scoring system meets AGFAPA-TBM.

Reports of COVID-19 Vaccine Adverse Events in Predominantly Republican vs Democratic States.

Importance: Antivaccine sentiment is increasingly associated with conservative political positions. Republican-inclined states exhibit lower COVID-19 vaccination rates, but the association between political inclination and reported vaccine adverse events (AEs) is unexplored. Objective: To assess whether there is an association between state political inclination and the reporting rates of COVID-19 vaccine AEs. Design, Setting, and Participants: This cross-sectional study used the AE reports after COVID-19 vaccination from the Vaccine Adverse Event Reporting System (VAERS) database from 2020 to 2022, with reports after influenza vaccines from 2019 to 2022 used as a reference. These reports were examined against state-level percentage of Republican votes in the 2020 US presidential election. Exposure: State-level percentage of Republican votes in the 2020 US presidential election. Main Outcomes and Measures: Rates of any AE among COVID-19 vaccine recipients, rates of any severe AE among vaccine recipients, and the proportion of AEs reported as severe. Results: A total of 620 456 AE reports (mean [SD] age of vaccine recipients, 51.8 [17.6] years; 435 797 reports from women [70.2%]; a vaccine recipient could potentially file more than 1 report, so reports are not necessarily from unique individuals) for COVID-19 vaccination were identified from the VAERS database. Significant associations between state political inclination and state AE reporting were observed for all 3 outcomes: a 10% increase in Republican voting was associated with increased odds of AE reports (odds ratio [OR], 1.05; 95% CI, 1.05-1.05; P < .001), severe AE reports (OR, 1.25; 95% CI, 1.24-1.26; P < .001), and the proportion of AEs reported as severe (OR, 1.21; 95% CI, 1.20-1.22; P < .001). These associations were seen across all age strata in stratified analyses and were more pronounced among older subpopulations. Conclusions and Relevance: This cross-sectional study found that the more states were inclined to vote Republican, the more likely their vaccine recipients or their clinicians reported COVID-19 vaccine AEs. These results suggest that either the perception of vaccine AEs or the motivation to report them was associated with political inclination.

Geldof Expert Consensus Classification of Perianal Fistulizing Crohn's Disease: A Real-World Application in a Serial Fistula MRI Cohort.

Background and Aims: Perianal fistulizing Crohn's disease (CD-PAF) is an aggressive phenotype of Crohn's disease (CD) defined by frequent relapses and disabling symptoms. A novel consensus classification system was recently outlined by Geldof et al. that seeks to unify disease severity with patient-centered goals but has not yet been validated. We aimed to apply this to a real-world cohort and identify factors that predict transition between classes over time. Methods: We identified all patients with CD-PAF and at least one baseline and one follow-up pelvic (pMRI). Geldof Classification, disease characteristics, and imaging indices were collected retrospectively at time periods corresponding with respective MRIs. Results: We identified 100 patients with CD-PAF of which 96 were assigned Geldof Classes 1 - 2c at baseline. Most patients (78.1%) started in Class 2b, but changes in classification were observed in 52.1% of all patients. Male sex (72.0%, 46.6%, 40.0%, p = 0.03) and prior perianal surgery (52.0% vs 44.6% vs 40.0%, p = 0.02) were more frequently observed in those with improved. Baseline pMRI indices were not associated with changes in classification, however, greater improvements in mVAI, MODIFI-CD, and PEMPAC were seen among those who improved. Linear mixed effect modeling identified only male sex (-0.31, 95% CI -0.60 to -0.02) with improvement in class. Conclusion: Geldof classification highlights the dynamic nature of CD-PAF over time, however, our ability to predict transitions between classes remains limited and requires prospective assessment. Improvement in MRI index scores over time was associated with a transition to lower Geldof classification.

Diagnostic efficacy of image-guided core needle biopsy of suspected malignant osseous lesions: a retrospective cohort study from a single academic institution.

OBJECTIVES: To evaluate diagnostic yield and accuracy of image-guided core needle biopsy (ICNB) of suspected malignant osseous lesions in a large cohort of adults, evaluate what factors influence these measures, and offer technical recommendations to optimize yield. METHODS: A retrospective analysis of 2321 ICNBs performed from 2010 to 2021 was completed. The diagnostic yield and accuracy of the biopsies as well as a series of patient, lesion-related, and technical factors were retrospectively analyzed. Multivariate statistical analysis was performed to evaluate what factors were associated with yield and accuracy. Different cutoff values of total core length and core number were then tested to determine threshold values in relation to increased diagnostic yield. RESULTS: Diagnostic yield was 98.2% (2279/2321) and accuracy was 97.6% (120/123). Increased total core length (odds ratio [OR] = 2.34, 95% confidence interval [CI] (1.41-3.90), p = 0.001), core number (OR = 1.51, 95% CI (1.06-2.16), p = 0.02) and presence of primary malignancy (OR = 2.81, 95% CI (1.40-5.62), p = 0.004) were associated with improved yield. Lesion location in an extremity (OR = 0.27, 95% CI (0.11-0.68), p = 0.006) and using fluoroscopic imaging guidance (OR = 0.33, 95% CI (0.12-0.90), p = 0.03) were associated with lower yield. Cutoff thresholds in relation to increased diagnostic yield were found to be 20 mm total core length (marginal OR = 4.16, 95% CI = (2.09-9.03), p < 0.001), and three total cores obtained (marginal OR = 2.78, 95% CI (1.34-6.54), p = 0.005). None of the analyzed factors influenced diagnostic accuracy. CONCLUSIONS: ICNB has a high rate of diagnostic yield and accuracy. Several factors influence diagnostic yield; 20 mm core length and three total cores optimize yield. CLINICAL RELEVANCE STATEMENT: Image-guided core needle biopsy of suspected malignant osseous lesions is a safe procedure with a very high rate of diagnostic yield and accuracy. Obtaining 20 mm total core length and three total cores optimizes diagnostic yield. KEY POINTS: • In a retrospective cohort study, image-guided core needle biopsy of suspected osseous malignant lesions in adults was found to have very high rates of diagnostic yield and accuracy. • Increased total core length and core number of biopsies were each associated with increased diagnostic yield, and these relationships reached thresholds at 20 mm total core length and three total cores obtained. • The presence of a known primary malignancy was also associated with increased yield while using fluoroscopic imaging guidance and lesion location in an extremity were associated with decreased yield.

One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites.

OBJECTIVE: To characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children's hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD). METHODS: In this study, we introduce the ODACoRH algorithm, a one-shot distributed algorithm designed for the competing risks model with heterogeneity. Our approach considers the variability in baseline hazard functions of multiple endpoints of interest across different sites. To accomplish this, we build a surrogate likelihood function by combining patient-level data from the local site with aggregated data from other external sites. We validated our method through extensive simulation studies and replication of the RISK study to investigate the impact of risk factors on the PCD for adolescents and children from four children's hospitals within the PEDSnet, A National Pediatric Learning Health System. To evaluate our ODACoRH algorithm, we compared results from the ODACoRH algorithms with those from meta-analysis as well as those derived from the pooled data. RESULTS: The ODACoRH algorithm had the smallest relative bias to the gold standard method (-0.2%), outperforming the meta-analysis method (-11.4%). In the PCD association study, the estimated subdistribution hazard ratios obtained through the ODACoRH algorithms are identical on par with the results derived from pooled data, which demonstrates the high reliability of our federated learning algorithms. From a clinical standpoint, the identified risk factors for PCD align well with the RISK study published in the Lancet in 2017 and other published studies, supporting the validity of our findings. CONCLUSION: With the ODACoRH algorithm, we demonstrate the capability of effectively integrating data from multiple sites in a decentralized data setting while accounting for between-site heterogeneity. Importantly, our study reveals several crucial clinical risk factors for PCD that merit further investigations.

Confidence score: a data-driven measure for inclusive systematic reviews considering unpublished preprints.

OBJECTIVES: COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain. MATERIALS AND METHODS: We propose a novel data-driven method for assigning weights to individual preprints in systematic reviews and meta-analyses. This weight termed the "confidence score" is obtained using the survival cure model, also known as the survival mixture model, which takes into account the time elapsed between posting and publication of a preprint, as well as metadata such as the number of first 2-week citations, sample size, and study type. RESULTS: Using 146 preprints on COVID-19 therapeutics posted from the beginning of the pandemic through April 30, 2021, we validated the confidence scores, showing an area under the curve of 0.95 (95% CI, 0.92-0.98). Through a use case on the effectiveness of hydroxychloroquine, we demonstrated how these scores can be incorporated practically into meta-analyses to properly weigh preprints. DISCUSSION: It is important to note that our method does not aim to replace existing measures of study quality but rather serves as a supplementary measure that overcomes some limitations of current approaches. CONCLUSION: Our proposed confidence score has the potential to improve systematic reviews of evidence related to COVID-19 and other clinical conditions by providing a data-driven approach to including unpublished manuscripts.

Associations of Vitamins and Related Cofactor Metabolites with Mammographic Breast Density in Premenopausal Women.

BACKGROUND: Identifying biological drivers of mammographic breast density (MBD), a strong risk factor for breast cancer, could provide insight into breast cancer etiology and prevention. Studies on dietary factors and MBD have yielded conflicting results. There are, however, very limited data on the associations of dietary biomarkers and MBD. OBJECTIVE: We aimed to investigate the associations of vitamins and related cofactor metabolites with MBD in premenopausal women. METHODS: We measured 37 vitamins and related cofactor metabolites in fasting plasma samples of 705 premenopausal women recruited during their annual screening mammogram at the Washington University School of Medicine, St. Louis, MO. Volpara was used to assess volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV). We estimated the least square means of VPD, DV, and NDV across quartiles of each metabolite, as well as the regression coefficient of a metabolite in continuous scale from multiple covariate-adjusted linear regression. We corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discover rate (FDR) at a 5% level. RESULTS: Participants' mean VPD was 10.5%. Two vitamin A metabolites (ß-cryptoxanthin and carotene diol 2) were positively associated, and one vitamin E metabolite (γ-tocopherol) was inversely associated with VPD. The mean VPD increased across quartiles of ß-cryptoxanthin (Q1 = 7.2%, Q2 = 7.7%, Q3 = 8.4%%, Q4 = 9.2%; P-trend = 1.77E-05, FDR P value = 1.18E-03). There was a decrease in the mean VPD across quartiles of γ-tocopherol (Q1 = 9.4%, Q2 = 8.1%, Q3 = 8.0%, Q4 = 7.8%; P -trend = 4.01E-03, FDR P value = 0.04). Seven metabolites were associated with NDV: 3 vitamin E (γ-CEHC glucuronide, δ-CEHC, and γ-tocopherol) and 1 vitamin C (gulonate) were positively associated, whereas 2 vitamin A (carotene diol 2 and ß-cryptoxanthin) and 1 vitamin C (threonate) were inversely associated with NDV. No metabolite was significantly associated with DV. CONCLUSION: We report novel associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

Quantifying and correcting bias due to outcome dependent self-reported weights in longitudinal study of weight loss interventions.

In response to the escalating global obesity crisis and its associated health and financial burdens, this paper presents a novel methodology for analyzing longitudinal weight loss data and assessing the effectiveness of financial incentives. Drawing from the Keep It Off trial-a three-arm randomized controlled study with 189 participants-we examined the potential impact of financial incentives on weight loss maintenance. Given that some participants choose not to weigh themselves because of small weight change or weight gains, which is a common phenomenon in many weight-loss studies, traditional methods, for example, the Generalized Estimating Equations (GEE) method tends to overestimate the effect size due to the assumption that data are missing completely at random. To address this challenge, we proposed a framework which can identify evidence of missing not at random and conduct bias correction using the estimating equation derived from pairwise composite likelihood. By analyzing the Keep It Off data, we found that the data in this trial are most likely characterized by non-random missingness. Notably, we also found that the enrollment time (i.e., duration time) would be positively associated with the weight loss maintenance after adjusting for the baseline participant characteristics (e.g., age, sex). Moreover, the lottery-based intervention was found to be more effective in weight loss maintenance compared with the direct payment intervention, though the difference was non-statistically significant. This framework's significance extends beyond weight loss research, offering a semi-parametric approach to assess missing data mechanisms and robustly explore associations between exposures (e.g., financial incentives) and key outcomes (e.g., weight loss maintenance). In essence, the proposed methodology provides a powerful toolkit for analyzing real-world longitudinal data, particularly in scenarios with data missing not at random, enriching comprehension of intricate dataset dynamics.

Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research.

Outcome-wide analysis can offer several benefits, including increased power to detect weak signals and the ability to identify exposures with multiple effects on health, which may be good targets for preventive measures. Recently, advanced statistical multivariate techniques for outcome-wide analysis have been developed, but they have been rarely applied to exposome analysis. In this work, we provide an overview of a selection of methods that are well-suited for outcome-wide exposome analysis and are implemented in the R statistical software. Our work brings together six different methods presenting innovative solutions for typical problems arising from outcome-wide approaches in the context of the exposome, including dependencies among outcomes, high dimensionality, mixed-type outcomes, missing data records, and confounding effects. The identified methods can be grouped into four main categories: regularized multivariate regression techniques, multi-task learning approaches, dimensionality reduction approaches, and bayesian extensions of the multivariate regression framework. Here, we compare each technique presenting its main rationale, strengths, and limitations, and provide codes and guidelines for their application to exposome data. Additionally, we apply all selected methods to a real exposome dataset from the Human Early-Life Exposome (HELIX) project, demonstrating their suitability for exposome research. Although the choice of the best method will always depend on the challenges to be faced in each application, for an exposome-like analysis we find dimensionality reduction and bayesian methods such as reduced rank regression (RRR) or multivariate bayesian shrinkage priors (MBSP) particularly useful, given their ability to deal with critical issues such as collinearity, high-dimensionality, missing data or quantification of uncertainty.

Implementing an Interdisciplinary Procedure Curriculum.

INTRODUCTION: This curriculum was designed to improve access to procedures for our internal medicine residents. METHODS: We created an interdisciplinary procedure course (IDPC) composed of two simulation sessions and a one-week procedural rotation supervised by multiple specialties including nephrology, cardiology, cardiothoracic anesthesiology, general anesthesiology, and interventional radiology. After the course, residents completed two surveys documenting the number of procedures and their level of confidence on a Likert scale (1 = very unconfident to 5 = very confident) prior to and after completing the curriculum. RESULTS: Sixteen residents participated in the course from September 2021 to June 2022. The collective number of procedures performed by these 16 residents increased from 176 to 343 after a one-week rotation. For arterial lines, the proportion of residents that reported an improvement in confidence scores was 0.44 (95% confidence interval 0.23 to 1, p-value of 0.60). The proportion of residents that had an increase in their confidence performing central lines was 0.63 (95% confidence interval 0.39 to 1, p-value of 0.23). For intubations, the proportion of residents that reported an improvement in confidence was 0.94 (95% confidence interval 0.72 to 1, p-value of 0.0006). CONCLUSION: By collaborating with multiple specialties, residents almost doubled the number of procedures performed during training and reported an increased level of confidence in procedural performance for airway intubation. We learned residents want to improve their access to procedures and described a curriculum that was easily implemented.

Study on creep characteristics and component model of saline soil in hexi corridor.

Accurate mastery of the creep characteristics of unsaturated saline soil is extremely important for the long-term stability and safe operation of all types of buildings. In this paper, the research object focused on the saline soil of the Zhangye area, Hexi corridor. The indoor triaxial CU creep test was carried out by means of graded loading to study the creep characteristics of saline soil under different salt content and loading stress. The Merchant and Burgers models were used to predict the creep behavior of the saline soils, and the predicted results were compared with the experimental values. The results showed that the triaxial creep curve of saline soil developed in stage III. Namely, transient creep stage, deceleration creep stage and steady-state creep stage. The creep deformation increases with the increase of salt content and loading stress. The stress-strain isochronous curve has non-linear growth, and the cluster of curves develops from dense to sparse after increasing to long-term strength (100∼150 kPa). The parameters of the Merchant and Burgers model vary with salt content and loading stress, and the creep curve predicted by the Burgers model is closer to the test value.

Lipidome of mammographic breast density in premenopausal women.

BACKGROUND: High mammographic breast density (MBD) is a strong risk factor for breast cancer development, but the biological mechanisms underlying MBD are unclear. Lipids play important roles in cell differentiation, and perturbations in lipid metabolism are implicated in cancer development. Nevertheless, no study has applied untargeted lipidomics to profile the lipidome of MBD. Through this study, our goal is to characterize the lipidome of MBD in premenopausal women. METHODS: Premenopausal women were recruited during their annual screening mammogram at the Washington University School of Medicine in St. Louis, MO. Untargeted lipidomic profiling for 982 lipid species was performed at Metabolon (Durham, NC®), and volumetric measures of MBD (volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)) was assessed using Volpara 1.5 (Volpara Health®). We performed multivariable linear regression models to investigate the associations of lipid species with MBD and calculated the covariate-adjusted least square mean of MBD by quartiles of lipid species. MBD measures were log10 transformed, and lipid species were standardized. Linear coefficients of MBD were back-transformed and considered significant if the Bonferroni corrected p-value was < 0.05. RESULTS: Of the 705 premenopausal women, 72% were non-Hispanic white, and 23% were non-Hispanic black. Mean age, and BMI were 46 years and 30 kg/m2, respectively. Fifty-six lipid species were significantly associated with VPD (52 inversely and 4 positively). The lipid species with positive associations were phosphatidylcholine (PC)(18:1/18:1), lysophosphatidylcholine (LPC)(18:1), lactosylceramide (LCER)(14:0), and phosphatidylinositol (PI)(18:1/18:1). VPD increased across quartiles of PI(18:1/18:1): (Q1 = 7.5%, Q2 = 7.7%, Q3 = 8.4%, Q4 = 9.4%, Bonferroni p-trend = 0.02). The lipid species that were inversely associated with VPD were mostly from the triacylglycerol (N = 43) and diacylglycerol (N = 7) sub-pathways. Lipid species explained some of the variation in VPD. The inclusion of lipid species increased the adjusted R2 from 0.45, for a model that includes known determinants of VPD, to 0.59. CONCLUSIONS: We report novel lipid species that are associated with MBD in premenopausal women. Studies are needed to validate our results and the translational potential.