Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA.

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

Modified furosemide responsiveness index and biomarkers for AKI progression and prognosis: a prospective observational study.

BACKGROUND: Modified furosemide responsiveness index (mFRI) is a novel biomarker for assessing diuretic response and AKI progression in patients with early AKI. However, the comparative predictive performance of mFRI and novel renal biomarkers for adverse renal outcomes remains unclear. In a single-center prospective study, we aimed to evaluate the discriminatory abilities of mFRI and other novel renal biomarkers in predicting AKI progression and prognosis in patients with initial mild and moderate AKI (KDIGO stage 1 to 2). RESULTS: Patients with initial mild and moderate AKI within 48 h following cardiac surgery were included in this study. The mFRI, renal biomarkers (including serum or urinary neutrophil gelatinase-associated lipocalin [sNGAL or uNGAL], serum cystatin C, urinary N-acetyl-beta-D-glycosaminidase [uNAG], urinary albumin-to-creatinine ratio) and cytokines (TNF, IL-1ß, IL-2R, IL-6, IL-8, and IL-10) were measured at AKI diagnosis. The mFRI was calculated for each patient, which was defined as 2-hour urine output divided by furosemide dose and body weight. Of 1013 included patients, 154 (15.2%) experienced AKI progression, with 59 (5.8%) progressing to stage 3 and 33 (3.3%) meeting the composite outcome of hospital mortality or receipt of renal replacement therapy (RRT). The mFRI showed non-inferiority or potential superiority to renal biomarkers and cytokines in predicting AKI progression (area under the curve [AUC] 0.80, 95% confidence interval [CI] 0.77-0.82), progression to stage 3 (AUC 0.87, 95% CI 0.85-0.89), and composite outcome of death and receipt of RRT (AUC 0.85, 95% CI 0.82-0.87). Furthermore, the combination of a functional biomarker (mFRI) and a urinary injury biomarker (uNAG or uNGAL) resulted in a significant improvement in the prediction of adverse renal outcomes than either individual biomarker (all P < 0.05). Moreover, incorporating these panels into clinical model significantly enhanced its predictive capacity for adverse renal outcomes, as demonstrated by the C index, integrated discrimination improvement, and net reclassification improvement (all P < 0.05). CONCLUSIONS: As a rapid, cost-effective and easily accessible biomarker, mFRI, exhibited superior or comparable predictive capabilities for AKI progression and prognosis compared to renal biomarkers in cardiac surgical patients with mild to moderate AKI. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04962412. Registered July 15, 2021, https://clinicaltrials.gov/ct2/show/NCT04962412?cond=NCT04962412&draw=2&rank=1 .

Association between inflammatory biomarkers and postoperative acute kidney injury after cardiac surgery in patients with preoperative renal dysfunction: a retrospective pilot analysis.

BACKGROUND: Acute kidney injury (AKI) represents a significant post-cardiac surgery complication, particularly prevalent among individuals with pre-existing renal dysfunction. Chronic kidney disease (CKD) is frequently accompanied by persistent, low-grade inflammation, which is known to exacerbate systemic stress responses during surgical procedures. This study hypothesizes that these inflammatory responses might influence the incidence and severity of postoperative acute kidney injury (AKI), potentially serving as a protective mechanism by preconditioning the kidney to stress. METHODS: This retrospective study enrolled patients with preoperative renal dysfunction (eGFR between 15 and 60 ml/min/1.73 m²) who underwent cardiac surgery between January 2020 and December 2022. Preoperative inflammatory biomarkers were evaluated. The primary outcome was the incidence of postoperative AKI, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Multivariate regression models and sensitivity analyses were conducted to ascertain the relationship between inflammatory biomarkers and AKI. Restricted cubic spines (RCS) was conducted to explore nonlinear associations between inflammatory biomarkers and AKI. RESULTS: AKI occurred in 53.4% (392/734) of patients, accompanied by significant mortality and length of hospital stay increases in cases of AKI (P < 0.005). After full adjustment of confounders, neutrophil percentage-to-albumin ratio (OR = 0.28), systemic inflammation response index (OR = 0.70), systemic immune inflammation index (OR = 0.69), neutrophil-to-lymphocyte ratio (OR = 0.70), monocyte/high-density lipoprotein cholesterol ratio (OR = 0.53), neutrophil/high-density lipoprotein cholesterol ratio (OR = 0.43) demonstrated an inverse association with AKI. Sensitivity analyses revealed that patients in the highest quartile of these biomarkers exhibited a significantly lower prevalence of AKI compared to those in the lowest quartile (p for trend < 0.05). The RCS analysis suggested an "Inverted U-shaped" association of both LnNPAR and LnSIRI with AKI. CONCLUSIONS: This study identified an inverse association between preoperative inflammatory biomarkers and postoperative AKI in patients with preoperative renal dysfunction. The findings implied that preoperative inflammation may play a protective role against postoperative AKI in this patient population undergoing cardiac surgery.

Development and external validation of a novel modality for rapid recognition of aortic dissection based on peripheral pulse oximetry waveforms.

BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency that is often misdiagnosed as other chest pain conditions. Physiologically, AD may cause abnormalities in peripheral blood flow, which can be detected using pulse oximetry waveforms. PURPOSE: This study aimed to assess the feasibility of identifying AD based on pulse oximetry waveforms and to highlight the key waveform features that play a crucial role in this diagnostic method. METHODS: This prospective study employed high-risk chest pain cohorts from two emergency departments. The initial cohort was enriched with AD patients (n = 258, 47% AD) for model development, while the second cohort consisted of chest pain patients awaiting angiography (n = 71, 25% AD) and was used for external validation. Pulse oximetry waveforms from the four extremities were collected for each patient. After data preprocessing, a recognition model based on the random forest algorithm was trained using patients' gender, age, and waveform difference features extracted from the pulse oximetry waveforms. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The importance of features was also assessed using Shapley Value and Gini importance. RESULTS: The model demonstrated strong performance in identifying AD in both the training and external validation sets. In the training set, the model achieved an area under the ROC curve of 0.979 (95% CI: 0.961-0.990), sensitivity of 0.918 (95% CI: 0.873-0.955), specificity of 0.949 (95% CI: 0.912-0.985), and accuracy of 0.933 (95% CI: 0.904-0.959). In the external validation set, the model attained an area under the ROC curve of 0.855 (95% CI: 0.720-0.965), sensitivity of 0.889 (95% CI: 0.722-1.000), specificity of 0.698 (95% CI: 0.566-0.812), and accuracy of 0.794 (95% CI: 0.672-0.878). Decision curve analysis (DCA) further showed that the model provided a substantial net benefit for identifying AD. The median mean and median variance of the four limbs' signals were the most influential features in the recognition model. CONCLUSIONS: This study demonstrated the feasibility and strong performance of identifying AD based on peripheral pulse oximetry waveforms in high-risk chest pain populations in the emergency setting. The findings also provided valuable insights for future human fluid dynamics simulations to elucidate the impact of AD on blood flow in greater detail.

Remifentanil vs. dexmedetomidine for cardiac surgery patients with noninvasive ventilation intolerance: a multicenter randomized controlled trial.

BACKGROUND: The optimal sedative regime for noninvasive ventilation (NIV) intolerance remains uncertain. The present study aimed to assess the efficacy and safety of remifentanil (REM) compared to dexmedetomidine (DEX) in cardiac surgery patients with moderate-to-severe intolerance to NIV. METHODS: In this multicenter, prospective, single-blind, randomized controlled study, adult cardiac surgery patients with moderate-to-severe intolerance to NIV were enrolled and randomly assigned to be treated with either REM or DEX for sedation. The status of NIV intolerance was evaluated using a four-point NIV intolerance score at different timepoints within a 72-h period. The primary outcome was the mitigation rate of NIV intolerance following sedation. RESULTS: A total of 179 patients were enrolled, with 89 assigned to the REM group and 90 to the DEX group. Baseline characteristics were comparable between the two groups, including NIV intolerance score [3, interquartile range (IQR) 3-3 vs. 3, IQR 3-4, p = 0.180]. The chi-squared test showed that mitigation rate, defined as the proportion of patients who were relieved from their initial intolerance status, was not significant at most timepoints, except for the 15-min timepoint (42% vs. 20%, p = 0.002). However, after considering the time factor, generalized estimating equations showed that the difference was statistically significant, and REM outperformed DEX (odds ratio = 3.31, 95% confidence interval: 1.35-8.12, p = 0.009). Adverse effects, which were not reported in the REM group, were encountered by nine patients in the DEX group, with three instances of bradycardia and six cases of severe hypotension. Secondary outcomes, including NIV failure (5.6% vs. 7.8%, p = 0.564), tracheostomy (1.12% vs. 0%, p = 0.313), ICU LOS (7.7 days, IQR 5.8-12 days vs. 7.0 days, IQR 5-10.6 days, p = 0.219), and in-hospital mortality (1.12% vs. 2.22%, p = 0.567), demonstrated comparability between the two groups. CONCLUSIONS: In summary, our study demonstrated no significant difference between REM and DEX in the percentage of patients who achieved mitigation among cardiac surgery patients with moderate-to-severe NIV intolerance. However, after considering the time factor, REM was significantly superior to DEX. Trial registration ClinicalTrials.gov (NCT04734418), registered on January 22, 2021. URL of the trial registry record: https://register. CLINICALTRIALS: gov/prs/app/action/SelectProtocol?sid=S000AM4S&selectaction=Edit&uid=U00038YX&ts=3&cx=eqn1z0 .

Effect of Delayed Remote Ischemic Preconditioning on Acute Kidney Injury and Outcomes in Patients Undergoing Cardiac Surgery: A Randomized Clinical Trial.

BACKGROUND: Remote ischemic preconditioning (RIPC) has 2 time windows for organ protection: acute and delayed. Previous studies have mainly focused on the organoprotective effects of acute RIPC. We aimed to determine whether delayed RIPC can reduce the occurrence of acute kidney injury (AKI) and postoperative complications in patients undergoing cardiac surgery. METHODS: This prospective, single-center, double-blind, randomized controlled trial involved 509 patients at high risk for AKI who were scheduled for elective cardiac surgery requiring cardiopulmonary bypass. Patients were randomized to receive RIPC (4 cycles of 5-minute inflation and 5-minute deflation on 1 upper arm with a blood pressure cuff) 24 hours before surgery or a sham condition (control group) that was induced by 4 cycles of 5-minute inflation to a pressure of 20 mm Hg followed by 5-minute cuff deflation. The primary end point was the incidence of AKI within the prior 7 days after cardiac surgery. The secondary end points included renal replacement therapy during hospitalization, change in urinary biomarkers of AKI and markers of myocardial injury, duration of intensive care unit stay and mechanical ventilation, and occurrence of nonfatal myocardial infarction, stroke, and all-cause mortality by day 90. RESULTS: A total of 509 patients (mean age, 65.2±8.2 years; 348 men [68.4%]) were randomly assigned to the RIPC group (n=254) or control group (n=255). AKI was significantly reduced in the RIPC group compared with the control group (69/254 [27.2%] versus 90/255 [35.3%]; odds ratio, 0.68 [95% CI, 0.47-1.00]; P=0.048). There were no significant between-group differences in the secondary end points of perioperative myocardial injury (assessed by the concentrations of cardiac troponin T, creatine kinase myocardial isoenzyme, and NT-proBNP [N-terminal pro-brain natriuretic peptide]), duration of stay in the intensive care unit and hospital, and occurrence of nonfatal myocardial infarction, stroke, and all-cause mortality by day 90. CONCLUSIONS: Among high-risk patients undergoing cardiac surgery, delayed RIPC significantly reduced the occurrence of AKI. REGISTRATION: URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2000035568.

A new biomechanical model of the mammal jaw based on load path analysis.

The primary function of the tetrapod jaw is to transmit jaw muscle forces to bite points. The routes of force transfer in the jaw have never been studied but can be quantified using load paths - the shortest, stiffest routes from regions of force application to support constraints. Here, we use load path analysis to map force transfer from muscle attachments to bite point and jaw joint, and to evaluate how different configurations of trabecular and cortical bone affect load paths. We created three models of the mandible of the Virginia opossum, Didelphis virginiana, each with a cortical bone shell, but with different material properties for the internal spaces: (1) a cortical-trabecular model, in which the interior space is modeled with bulk properties of trabecular bone; (2) a cortical-hollow model, in which trabeculae and mandibular canal are modeled as hollow; and (3) a solid-cortical model, in which the interior is modeled as cortical bone. The models were compared with published in vivo bite force and bone strain data, and the load paths calculated for each model. The trabecular model, which is preferred because it most closely approximates the actual morphology, was best validated by in vivo data. In all three models, the load path was confined to cortical bone, although its route within the cortex varied depending on the material properties of the inner model. Our analysis shows that most of the force is transferred through the cortical, rather than trabecular bone, and highlights the potential of load path analysis for understanding form-function relationships in the skeleton.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

An atlas of genetic effects on the monocyte methylome across European and African populations.

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Assessing the predictive value of elevated postoperative syndecan-1 levels for progressive acute kidney injury and kidney replacement therapy necessity in adult cardiac surgery patients.

BACKGROUND: The development of acute kidney injury (AKI) post-cardiac surgery significantly increases patient morbidity and healthcare costs. Prior researches have established Syndecan-1 (SDC-1) as a potential biomarker for endothelial injury and subsequent acute kidney injury development. This study assessed whether postoperative SDC-1 levels could further predict AKI requiring kidney replacement therapy (AKI-KRT) and AKI progression. METHODS: In this prospective study, 122 adult cardiac surgery patients, who underwent valve or coronary artery bypass grafting (CABG) or a combination thereof and developed AKI within 48 h post-operation from May to September 2021, were monitored for the progression to stage 2-3 AKI or the need for KRT. We analyzed the predictive value of postoperative serum SDC-1 levels in relation to multiple endpoints. RESULTS: In the study population, 110 patients (90.2%) underwent cardiopulmonary bypass, of which thirty received CABG or combined surgery. Fifteen patients (12.3%) required KRT, and thirty-eight (31.1%) developed progressive AKI, underscoring the severe AKI incidence. Multivariate logistic regression indicated that elevated SDC-1 levels were independent risk factors for progressive AKI (OR = 1.006) and AKI-KRT (OR = 1.011). The AUROC for SDC-1 levels in predicting AKI-KRT and AKI progression was 0.892 and 0.73, respectively, outperforming the inflammatory cytokines. Linear regression revealed a positive correlation between SDC-1 levels and both hospital (ß = 0.014, p = 0.022) and ICU stays (ß = 0.013, p < 0.001). CONCLUSION: Elevated postoperative SDC-1 levels significantly predict AKI progression and AKI-KRT in patients following cardiac surgery. The study's findings support incorporating SDC-1 level monitoring into post-surgical care to improve early detection and intervention for severe AKI.

Predictive value of perioperative NT-proBNP levels for acute kidney injury in patients with compromised renal function undergoing cardiac surgery: a case control study.

BACKGROUND: Acute kidney injury (AKI) significantly increases morbidity and mortality following cardiac surgery, especially in patients with pre-existing renal impairments. N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a marker of cardiac stress and dysfunction, conditions often exacerbated during cardiac surgery and prevalent in chronic kidney disease (CKD) patients. Elevated NT-proBNP levels can indicate underlying cardiac strain, hemodynamic instability and volume overload. This study evaluated the association between perioperative changes in NT-proBNP levels and the incidence of AKI in this particular patient group. METHODS: This retrospective study involved patients with impaired renal function (eGFR 15-60 ml/min/1.73 m²) who underwent cardiac surgery from July to December 2022. It analyzed the association between the ratio of preoperative and ICU admittance post-surgery NT-proBNP levels and the development of AKI and AKI stage 2-3, based on KDIGO criteria, using multivariate logistic regression models. Restricted cubic spline analysis assessed non-linear associations between NT-proBNP and endpoints. Subgroup analysis was performed to assess the heterogeneity of the association between NT-proBNP and endpoints in subgroups. RESULTS: Among the 199 participants, 116 developed postoperative AKI and 16 required renal replacement therapy. Patients with AKI showed significantly higher postoperative NT-proBNP levels compared to those without AKI. Decreased baseline eGFR and increased post/preoperative NT-proBNP ratios were associated with higher AKI risk. Specifically, the highest quantile post/preoperative NT-proBNP ratio indicated an approximately seven-fold increase in AKI risk and a ninefold increase in AKI stage 2-3 risk compared to the lowest quantile. The area under the receiver operating characteristic curve for predicting AKI and AKI stage 2-3 using NT-proBNP were 0.63 and 0.71, respectively, demonstrating moderate accuracy. Subgroup analysis demonstrated that the positive association between endpoints and logarithmic transformed post/preoperative NT-proBNP levels was consistently robust in subgroup analyses stratified by age, sex, surgery, CPB application, hypertension, diabetes status and fluid balance. CONCLUSION: Perioperative NT-proBNP level changes are predictive of postoperative AKI in patients with pre-existing renal deficiencies undergoing cardiac surgery, aiding in risk assessment and patient management.

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Structural basis of antagonist selectivity in endothelin receptors.

Endothelins and their receptors, ETA and ETB, play vital roles in maintaining vascular homeostasis. Therapeutically targeting endothelin receptors, particularly through ETA antagonists, has shown efficacy in treating pulmonary arterial hypertension (PAH) and other cardiovascular- and renal-related diseases. Here we present cryo-electron microscopy structures of ETA in complex with two PAH drugs, macitentan and ambrisentan, along with zibotentan, a selective ETA antagonist, respectively. Notably, a specialized anti-ETA antibody facilitated the structural elucidation. These structures, together with the active-state structures of ET-1-bound ETA and ETB, and the agonist BQ3020-bound ETB, in complex with Gq, unveil the molecular basis of agonist/antagonist binding modes in endothelin receptors. Key residues that confer antagonist selectivity to endothelin receptors were identified along with the activation mechanism of ETA. Furthermore, our results suggest that ECL2 in ETA can serve as an epitope for antibody-mediated receptor antagonism. Collectively, these insights establish a robust theoretical framework for the rational design of small-molecule drugs and antibodies with selective activity against endothelin receptors.

Jurassic fossil juvenile reveals prolonged life history in early mammals.

Living mammal groups exhibit rapid juvenile growth with a cessation of growth in adulthood1. Understanding the emergence of this pattern in the earliest mammaliaforms (mammals and their closest extinct relatives) is hindered by a paucity of fossils representing juvenile individuals. We report exceptionally complete juvenile and adult specimens of the Middle Jurassic docodontan Krusatodon, providing anatomical data and insights into the life history of early diverging mammaliaforms. We used synchrotron X-ray micro-computed tomography imaging of cementum growth increments in the teeth2-4 to provide evidence of pace of life in a Mesozoic mammaliaform. The adult was about 7 years and the juvenile 7 to 24 months of age at death and in the process of replacing its deciduous dentition with its final, adult generation. When analysed against a dataset of life history parameters for extant mammals5, the relative sequence of adult tooth eruption was already established in Krusatodon and in the range observed in extant mammals but this development was prolonged, taking place during a longer period as part of a significantly longer maximum lifespan than extant mammals of comparable adult body mass (156 g or less). Our findings suggest that early diverging mammaliaforms did not experience the same life histories as extant small-bodied mammals and the fundamental shift to faster growth over a shorter lifespan may not have taken place in mammaliaforms until during or after the Middle Jurassic.

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.

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.

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.

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.