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.

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.

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.

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.

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.

Privacy-preserving harmonization via distributed ComBat.

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

EMBRACE: An EM-based bias reduction approach through Copas-model estimation for quantifying the evidence of selective publishing in network meta-analysis.

Systematic reviews and meta-analyses synthesize results from well-conducted studies to optimize healthcare decision-making. Network meta-analysis (NMA) is particularly useful for improving precision, drawing new comparisons, and ranking multiple interventions. However, recommendations can be misled if published results are a selective sample of what has been collected by trialists, particularly when publication status is related to the significance of the findings. Unfortunately, the missing-not-at-random nature of this problem and the numerous parameters involved in modeling NMAs pose unique computational challenges to quantifying and correcting for publication bias, such that sensitivity analysis is used in practice. Motivated by this important methodological gap, we developed a novel and stable expectation-maximization (EM) algorithm to correct for publication bias in the network setting. We validate the method through simulation studies and show that it achieves substantial bias reduction in small to moderately sized NMAs. We also calibrate the method against a Bayesian analysis of a published NMA on antiplatlet therapies for maintaining vascular patency.

Accounting for publication bias using a bivariate trim and fill meta-analysis procedure.

In research synthesis, publication bias (PB) refers to the phenomenon that the publication of a study is associated with the direction and statistical significance of its results. Consequently, it may lead to biased (commonly optimistic) estimates of treatment effects. Visualization tools such as funnel plots have been widely used to investigate PB in univariate meta-analyses. The trim and fill procedure is a nonparametric method to identify and adjust for PB. It is popular among applied scientists due to its simplicity. However, most visualization tools and PB correction methods focus on univariate outcomes. For a meta-analysis with multiple outcomes, the conventional univariate trim and fill method can only account for different outcomes separately and thus may lead to inconsistent conclusions. In this article, we propose a bivariate trim and fill procedure to simultaneously account for PB in the presence of two outcomes that are possibly associated. Based on a recently developed galaxy plot for bivariate meta-analysis, the proposed procedure uses a data-driven imputation algorithm to detect and adjust PB. The method relies on the symmetry of the galaxy plot and assumes that some studies are suppressed based on a linear combination of outcomes. The method projects bivariate outcomes along a particular direction, uses the univariate trim and fill method to estimate the number of trimmed and filled studies, and yields consistent conclusions about PB. The proposed approach is validated using simulated data and is applied to a meta-analysis of the efficacy and safety of antidepressant drugs.

The effect on brain volume in HIV-negative and non-transplant cryptococcal meningitis.

To explore the brain volume (BV) changes of HIV-negative and non-transplant cryptococcal meningitis (CM) in 1 year after initial therapy. Case data were collected from 78 CM patients who underwent magnetic resonance imaging (MRI) scanning at least 3 times in 1-year interval after initial therapy. The assessment of BV was measured by a non-commercial software, uAI Research Portal. Linear mixed model was used to investigate the association between clinical characteristics and the changes in BV. Longitudinal study showed a decrease in total brain volume (-4.65 cm3, P = .005), regional brain volume including white matter (-2.86 cm3, P = .031) and basal ganglia (-0.25 cm3, P = .007), and increase in cerebrospinal fluid (CSF) volume (3.58 cm3, P = .013) in CM patients in 1 year after initial therapy. Ventricular volume in patients with ventriculoperitoneal shunts (VPS) was lower than that in patients without VPS (-7.5 cm3, P < .05). Ventricular volume in patients with post-infectious inflammatory response syndrome (PIIRS) was larger than that in patients without PIIRS (7.1 cm3, P < .01). In addition, temporal lobe atrophy was associated with corticosteroid therapy (-6.8 cm3, P < .01). The present study suggested that brain atrophy, especially regional BV decrease, could happen in HIV-negative and non-transplant CM patients over a 1-year interval.

We investigated the evolution of brain volume changes in different regions among HIV-negative and non-transplant cryptococcal meningitis (CM) patients within 1 year after initial therapy. To assess whether brain atrophy occurs among HIV-negative and non-transplant CM patients.

Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.

BACKGROUND: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. OBJECTIVE: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. METHODS: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. RESULTS: In simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. CONCLUSIONS: ODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.

Density- and moisture-dependent effects of arbuscular mycorrhizal fungus on drought acclimation in wheat.

Arbuscular mycorrhizal fungus (AMF) is widely viewed as an ecosystem engineer to help plants adapt to adverse environments. However, a majority of the previous studies regarding AMF's eco-physiological effects are mutually inconsistent. To clarify this fundamental issue, we conducted an experiment focused on wheat (Triticum aestivum L.) plants with or without AMF (Funneliformis mosseae) inoculation. Two water regimes (80% and 40% field water capacity, FWC80 (CK) and FWC40 (drought stress) and four planting densities (6 or 12 plants per pot as low densities, 24 or 48 plants per pot as high densities) were designed. AMF inoculation did not show significant effects on shoot biomass, grain yield, and water use efficiency (WUE) under the low densities, regardless of water regimes. However, under the high densities, AMF inoculation significantly decreased shoot biomass, grain yield and WUE in FWC80, while it significantly increased these parameters in FWC40, showing density and/or moisture-dependent effects of AMF on wheat performance. In FWC40, the relationships between reproductive biomass (y-axis) vs. vegetative biomass (x-axis) (R-V), and between grain biomass (y-axis, sink) vs. leaf biomass (x-axis, source) fell into a typical allometric pattern (α > 1, P < 0.001), and the AMF inoculation significantly increased the values of α. Yet in FWC80, they were in an isometric pattern (α ≈ 1, P < 0.001) and AMF addition had no significant effects on α. Similarly, AMF did not significantly change the isometric relationship between leaf biomass (i.e., metabolic rate) and shoot biomass (body size) in FWC80, while it significantly decreased the α of allometric relationship between both of them in FWC40 (α > 1, P < 0.001). We therefore, sketched a generalized model of R-V and sink-source relationships as affected by AMF, in which AMF inoculation might enhance the capabilities of sink acquisition and utilization under drought stress, while having no significant effect under the well watered conditions. Our findings demonstrate dual density- and moisture-dependent effects of AMF on plant development and provide new insights into current ecological applications of AMF as an ecosystem engineer.

Prediction of post-vaccination Guillain-Barré syndrome using data from a passive surveillance system.

PURPOSE: Severe adverse events (AEs), such as Guillain-Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3). METHODS: This study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance. RESULTS: Of the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty-four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS. CONCLUSION: The prediction performance demonstrated the potential of developing risk-prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset.

Canonical variate regression.

In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study.

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.

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.

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.

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.

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.