Catalytic Asymmetric Dearomative [2 + 2] Photocycloaddition/Ring-Expansion Sequence of Indoles with Diversified Alkenes.

Developing novel strategies for catalytic asymmetric dearomatization (CADA) reactions is highly valuable. Visible light-mediated photocatalysis is demonstrated to be a powerful tool to activate aromatic compounds for further synthetic transformations. Herein, a catalytic asymmetric dearomative [2 + 2] photocycloaddition/ring-expansion sequence of indoles with simple alkenes was reported, providing a facile access to enantioenriched cyclopenta[b]indoles with good to high yields and enantioselectivities by means of chiral lanthanide photocatalysis. This protocol exhibited a broad substrate scope and good functional group tolerance, as well as potential applications in the synthesis of bioactive molecules. Mechanistic studies, including control experiments, UV-vis absorption spectroscopy, emission spectroscopy, and DFT calculations, were carried out, shedding insights into the reaction mechanism and the origin of enantioselectivity.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

AKI-Pro score for predicting progression to severe acute kidney injury or death in patients with early acute kidney injury after cardiac surgery.

BACKGROUND: No reliable clinical tools exist to predict acute kidney injury (AKI) progression. We aim to explore a scoring system for predicting the composite outcome of progression to severe AKI or death within seven days among early AKI patients after cardiac surgery. METHODS: In this study, we used two independent cohorts, and patients who experienced mild/moderate AKI within 48 h after cardiac surgery were enrolled. Eventually, 3188 patients from the MIMIC-IV database were used as the derivation cohort, while 499 patients from the Zhongshan cohort were used as external validation. The primary outcome was defined by the composite outcome of progression to severe AKI or death within seven days after enrollment. The variables identified by LASSO regression analysis were entered into logistic regression models and were used to construct the risk score. RESULTS: The composite outcome accounted for 3.7% (n = 119) and 7.6% (n = 38) of the derivation and validation cohorts, respectively. Six predictors were assembled into a risk score (AKI-Pro score), including female, baseline eGFR, aortic surgery, modified furosemide responsiveness index (mFRI), SOFA, and AKI stage. And we stratified the risk score into four groups: low, moderate, high, and very high risk. The risk score displayed satisfied predictive discrimination and calibration in the derivation and validation cohort. The AKI-Pro score discriminated the composite outcome better than CRATE score, Cleveland score, AKICS score, Simplified renal index, and SRI risk score (all P < 0.05). CONCLUSIONS: The AKI-Pro score is a new clinical tool that could assist clinicians to identify early AKI patients at high risk for AKI progression or death.

Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.

Introduction: Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods: By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results: By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion: In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Clinical Presentations and Intense FDG-avidity in Pulmonary Angiomatoid Fibrous Histiocytoma with Potential Diagnostic Pitfalls: A Case Report and Literature Review.

INTRODUCTION: Angiomatoid fibrous histiocytoma (AFH) is a borderline tumor usually affecting the the children or young adults. 18F-Fluorodexoyglucose (FDG) positron emission tomography/computed tomography (PET/CT) investigations of pulmonary AFH are rare, and there are currently no reports of intense FDG uptake in AFH. CASE REPORT: We report an AFH that occurred in the lung of a 57-year-old woman. She presented with paroxysmal cough and occasional bloodshot sputum. 18FFDG PET/CT revealed a right parahilar nodule with intense FDG-avidity, middle lobe atelectasis, and several bilateral axillary lymph nodes with mild hypermetabolic activity. This patient underwent a right middle lobe lobectomy via video-assisted thoracoscopy. Histopathologically, the diagnosis was pulmonary AFH. She had an uneventful postoperative course, and the bilateral axillary lymph nodes regressed during postoperative follow-up. CONCLUSIONS: The clinical presentation and image findings of patients with primary pulmonary AFH may be potential diagnosis pitfalls. The diagnosis of lymph nodes or distant metastases should be approached with caution. To avoid misdiagnosis, biopsy with histological examination and immunohistochemichal staining should be performed as early as possible.

18F-DGPET/CT Imaging of Ovarian Yolk Sac Tumor in a Woman after Induction of Labor: A Case Report.

Introduction: Ovarian yolk sac tumor (OYST) during pregnancy is rare and usually missed. There are few PET/CT studies on OYST in the literature. We reported a case of OYST detected by 18F-FDG PET/CT in a woman after induction of labor. Case Presentation: A 19-year-old woman after induction of labor because of severe malformation presented with abdominopelvic mass, laboratory tests revealed significantly elevated serum alpha-fetoprotein (AFP) level and elevated carbohydrate antigen 125 level. Abdomino-pelvic CT showed a cysticsolid mass of 82×152×167mm arising from the right ovary with abundant intratumoral vessels and intense enhancement in the solid part. Further evaluation of 18F-FDG PET/CT imaging showed significantly increased 18FDG uptake (SUVmax7.7) by the solid component of the ovarian mass and slight 18FDG-avid perihepatic effusion. The mass was resected and was confirmed to be the right OYST, After four courses of chemotherapy, the patient was followed up by PET/CT and had a complete metabolic response. Discussion: 18F-FDG PET/CT is a useful imaging modality for diagnosis and evaluation of OYST.

Skin mottling score assesses peripheral tissue hypoperfusion in critically ill patients following cardiac surgery.

BACKGROUND: Skin mottling is a common manifestation of peripheral tissue hypoperfusion, and its severity can be described using the skin mottling score (SMS). This study aims to evaluate the value of the SMS in detecting peripheral tissue hypoperfusion in critically ill patients following cardiac surgery. METHODS: Critically ill patients following cardiac surgery with risk factors for tissue hypoperfusion were enrolled (n = 373). Among these overall patients, we further defined a hypotension population (n = 178) and a shock population (n = 51). Hemodynamic and perfusion parameters were recorded. The primary outcome was peripheral hypoperfusion, defined as significant prolonged capillary refill time (CRT, > 3.0 s). The characteristics and hospital mortality of patients with and without skin mottling were compared. The area under receiver operating characteristic curves (AUROC) were used to assess the accuracy of SMS in detecting peripheral hypoperfusion. Besides, the relationships between SMS and conventional hemodynamic and perfusion parameters were investigated, and the factors most associated with the presence of skin mottling were identified. RESULTS: Of the 373-case overall population, 13 (3.5%) patients exhibited skin mottling, with SMS ranging from 1 to 5 (5, 1, 2, 2, and 3 cases, respectively). Patients with mottling had lower mean arterial pressure, higher vasopressor dose, less urine output (UO), higher CRT, lactate levels and hospital mortality (84.6% vs. 12.2%, p < 0.001). The occurrences of skin mottling were higher in hypotension population and shock population, reaching 5.6% and 15.7%, respectively. The AUROC for SMS to identify peripheral hypoperfusion was 0.64, 0.68, and 0.81 in the overall, hypotension, and shock populations, respectively. The optimal SMS threshold was 1, which corresponded to specificities of 98, 97 and 91 and sensitivities of 29, 38 and 67 in the three populations (overall, hypotension and shock). The correlation of UO, lactate, CRT and vasopressor dose with SMS was significant, among them, UO and CRT were identified as two major factors associated with the presence of skin mottling. CONCLUSION: In critically ill patients following cardiac surgery, SMS is a very specific yet less sensitive parameter for detecting peripheral tissue hypoperfusion.

Inhibition of colon C5a/C5a receptor signalling pathway confers protection against LPS-induced acute kidney injury via gut microbiota-kidney axis.

Acute kidney injury (AKI) is a critical condition often associated with systemic inflammation and dysregulated gut microbiota. This study aimed to investigate the effects of the C5a receptor antagonist W54011 on lipopolysaccharide (LPS)-induced AKI, focusing on the colon's C5a/C5a receptor pathway, intestinal barrier integrity, and gut microbiota. Our findings demonstrate that W54011 effectively ameliorated kidney injury in the LPS-induced AKI model by selectively inhibiting the colon's C5a/C5a receptor signalling pathway. Additionally, C5a receptor blockade resulted in the inhibition of colonic inflammation and the reconstruction of the intestinal mucosal barrier. Furthermore, W54011 administration significantly impacted the composition and stability of the gut microbiota, restoring the abundance of dominant bacteria to levels observed in the normal state of the intestinal flora and reducing the abundance of potentially harmful bacterial groups. In conclusion, W54011 alleviates LPS-induced AKI by modulating the interplay between the colon, gut microbiota, and kidneys. It preserves the integrity of the intestinal barrier and reinstates gut microbiota, thereby mitigating AKI symptoms. These findings suggest that targeting the colon and gut microbiota could be a promising therapeutic strategy for AKI treatment.

18F-FDG PET/CT imaging in pulmonary sarcomatoid carcinoma.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare highly aggressive and poorly differentiated non-small cell carcinoma, and little is known about the information on the usefulness of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT). We investigated the clinical and 18F-FDG PET/CT features of PSC. Methods: We retrospectively analyzed 25 consecutive PSC patients who had undergone 18F-FDG PET/CT. Demographic data, PET/CT findings before treatment, pathological features, and prognosis in these patients were investigated to define correlates between maximal standard uptake value (SUVmax) and clinicopathological parameters. Results: From March 2017 to January 2023, twenty-five eligible patients with PSC were identified. There were 23 (92%) men, aged 68.5 ± 8.5 (range 56-90) years. Eighteen (72%) patients had a frequent smoking history. The mean size of PSCs was 59.3 ± 18.6 (range 29-97) mm, and 23 (92%) PSCs were Stage IV tumors. 20 (80%) lesions were located in the upper lung and 19 (76%) cases belonged to the peripheral type. Necrotic foci appeared in 21(84%) tumors. 11 (44%) PSCs invaded the pleura. All PSCs were FDG avid, and the mean of SUVmax was 11.8 ± 5.3 (range 4.8-25.5). Metastases were found on PET/CT in 24(96%) patients. The SUVmax of the lesions ≥ 5cm was higher than that of the lesions < 5cm (p=0.004), and the SUVmax of lesions with TTF-1 expression was higher than those of lesions without TTF-1 expression (p=0.009). All of the 25 primary lesions were considered malignant and confirmative, probable, and possible diagnosis of PSC was made in 2 (8%), 4 (16%), and 5(20%) patients, respectively on PET/CT. PSC was not considered in 14 (56%) patients, in PET/CT. The survival of patients with surgery didn't demonstrate a significantly good prognosis as compared with those without surgery (p=0.675). Conclusion: All PSCs had obvious FDG avidity. Although imaging diagnosis is still difficult, combined clinical and imaging features more than 40% of primary lesions were considered for the possibility of PSC in our group. Early histopathological diagnosis is necessary to help develop a reasonable regimen.

Pre-procedure 18F-FDG PET/CT imaging improves the performance of CT-guided transthoracic biopsy.

PURPOSE: To compare computed tomography (CT)-guided transthoracic lung biopsies (CTLB) with and without pre-procedure 18F-fluorodeoxgyglucose positron emission tomography (18F-FDG PET)/CT images in the diagnosis of pulmonary nodules/masses. METHODS: This is a case-control study in a single center. The data of patients with a transthoracic lung biopsy guided by CT and pre-procedure 18F-FDG PET/CT (group 2, here called the "PETCTLB" group), including demographics, clinical characteristics, and biopsy-related parameters, were collected. The PET/CT scan was performed within 15 days before the biopsy. The data from patients with CTLB were used as controls (group 1). Biopsies for all patients were performed by the same physician between January 2019 and December 2021. The final diagnosis was based on surgical outcomes, or imaging findings, and the results of at least one 6-month follow-up. The demographics and clinical characteristics of patients, lesions and biopsy-related variables, diagnostic yields, and incidence of complications were compared between the two groups. Two-tailed t-tests were used to compare the mean values in the two independent groups, while categorical variables were compared using the Pearson chi-squared test, and P values < 0.05 were considered to be significant. RESULTS: A total of 84 patients were included, and 84 biopsies of 84 lung nodules/masses were analyzed. The demographics and clinical characteristics of group 2 (n = 39; 21 men; mean age, 63.2 ± 9.29 years) and group 1 (n = 45; 30 men; mean age, 61.2 ± 12.3 years) had no significant difference (P = 0.230 and 0.397, respectively). The procedure duration (11.1 ± 3.0 vs. 12.9 ± 3.3 minutes, P = 0.008), the number of samples (2.6 ± 0.5 vs. 3.1 ± 0.4, P < 0.001), diagnostic accuracy (97.4% vs. 82.2%, P = 0.033), and bleeding complication (25.6% vs. 42.2%, P = 0.034) of group 2 and group 1 were statistically different. CONCLUSION: A biopsy guided by CT plus pre-procedure 18F-FDG PET/CT (PETCTLB) is a safe procedure that can provide a precise diagnosis in the majority of lung nodules/masses. It has better diagnostic performance than CTLB.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Furosemide Responsiveness Predicts Acute Kidney Injury Progression After Cardiac Surgery.

BACKGROUND: As patients with acute kidney injury (AKI) progress to a higher stage, the risk for poor outcomes dramatically rises. Early identification of patients at high risk for AKI progression remains a major challenge. This study aimed to evaluate the value of furosemide responsiveness (FR) for predicting AKI progression in patients with initial mild and moderate AKI after cardiac surgery. METHODS: We performed 2 separate exploratory analyses. The Zhongshan cohort was a single-center, prospective, observational cohort, whereas the Beth Israel Deaconess Medical Center cohort was a single-center, retrospective cohort. We calculated 2 FR parameters for each patient, namely the FR index and modified FR index, defined as 2-hour urine output divided by furosemide dose (FR index, mL/mg/2 h) and by furosemide dose and body weight (modified FR index, mL/[mg·kg]/2 h), respectively. The primary outcome was AKI progression within 7 days. RESULTS: AKI progression occurred in 80 (16.0%) and 359 (11.3%) patients in the Zhongshan and Beth Israel Deaconess Medical Center cohorts, respectively. All FR parameters (considered continuously or in quartiles) were inversely associated with risk of AKI progression in both cohorts (all adjusted P < .01). The addition of FR parameters significantly improved prediction for AKI progression based on baseline clinical models involving C-index, net reclassification improvement, and integrated discrimination improvement index in both cohorts (all P < .01). CONCLUSIONS: FR parameters were inversely associated with risk of AKI progression in patients with mild and moderate AKI after cardiac surgery. The addition of FR parameters significantly improved prediction for AKI progression based on baseline clinical models.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Metabolomic profiles associated with physical activity in White and African American adult men.

BACKGROUND: Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations. OBJECTIVE: This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men. METHODS: Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking. RESULTS: Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups. CONCLUSIONS: Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

The sensitivity and specificity of 18F-FDG PET/CT in spinal leptomeningeal metastases: the synergistic effect of the 18F-FDG PET-CT to gadolinium-enhanced MRI.

Background: Magnetic resonance imaging (MRI) plays an important role in the diagnosis of leptomeningeal metastases (LM); however, some sub-centimeter lesions may be missed. Positron emission tomography/computed tomography (PET/CT) has a high sensitivity and may play a synergistic role with MRI in diagnosing spinal LM (SLM). We aimed to retrospectively evaluate the detection of SLM with 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) compared to that of whole spinal cord MRI in a single center. Methods: Patients with SLM who had undergone 18F-FDG PET/CT and MRI were enrolled. 18F-FDG PET/CT imaging findings were independently reviewed by 2 nuclear medicine physicians. 18F-FDG PET/CT findings of SLMs were described. A consistency test was conducted to assess the patient-based diagnostic results obtained by the 2 physicians. Patient-based sensitivity, accuracy, and specificity in diagnosing SLM between 18F-FDG PET/CT and MRI of the whole spinal cord were compared using the chi-square or Fisher's exact test. A P value of <0.05 was considered statistically significant. The receiver operating characteristic (ROC) curve was obtained to assess the diagnostic performance of maximum standardized uptake value (SUVmax) to diagnose SLM. Results: A total of 16 patients with SLM were included in this study from October 2010 to April 2022. The primary tumor involved the lungs, liver, ovaries, prostate, esophagus, and unknown primary site. The mean age of patients, including 13 males and 3 females, was 57.8±11.2 (range, 34-73) years. Of 16 patients with SLM, 10 had nodular diseases, 2 had linear diseases, and 4 had mixed diseases. The kappa value of the consistency test of the 2 radiologists' diagnostic results was 0.765. The patient-based sensitivity, specificity, and accuracy of 18F-FDG PET/CT in diagnosing SLM were 87.5%, 89.2%, and 88.7%, respectively and those of whole spinal cord MRI were 75.0%, 100.0%, and 92.5%, respectively. There were no significant differences in sensitivity, specificity, and accuracy between the 2 methods, with P values of 0.654, 0.115, and 0.506, respectively. However, more nodular diseases were observed on PET/CT. The area under the ROC curve (AUC) for the prediction of SLM by SUVmax was 0.907 [95% confidence interval (CI): 0.831-0.983]. When SUVmax ≥2.45, the Youden index was the largest, and the sensitivity and specificity were 89.3% and 75.7%, respectively. Conclusions: 18F-FDG PET/CT is a good choice of imaging modality for assessing SLM. In the diagnosis of SLMs, PET/CT and enhanced MRI can play a better synergistic role.

Gut microbiota impacts bone via Bacteroides vulgatus-valeric acid-related pathways.

Although the gut microbiota has been reported to influence osteoporosis risk, the individual species involved, and underlying mechanisms, remain largely unknown. We performed integrative analyses in a Chinese cohort of peri-/post-menopausal women with metagenomics/targeted metabolomics/whole-genome sequencing to identify novel microbiome-related biomarkers for bone health. Bacteroides vulgatus was found to be negatively associated with bone mineral density (BMD), which was validated in US white people. Serum valeric acid (VA), a microbiota derived metabolite, was positively associated with BMD and causally downregulated by B. vulgatus. Ovariectomized mice fed B. vulgatus demonstrated increased bone resorption and poorer bone micro-structure, while those fed VA demonstrated reduced bone resorption and better bone micro-structure. VA suppressed RELA protein production (pro-inflammatory), and enhanced IL10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells and enhanced maturation of osteoblasts in vitro. The findings suggest that B. vulgatus and VA may represent promising targets for osteoporosis prevention/treatment.