A biomechanical study of a polymer material bundled rib fracture fixator.

BACKGROUND: Rib fractures are one of the most common blunt injuries, accounting for approximately 10% of all trauma patients and 60% of thoracic injuries. Multiple rib fractures, especially flail chest, can cause local chest wall softening due to the loss of rib support, leading to paradoxical breathing, severe pain, and a high likelihood of accompanying lung contusions. OBJECTIVE: This study investigates the mechanical properties of a new polymer material rib internal fixator to provide theoretical data for its clinical use. METHODS: We conducted in vitro mechanical tests on 20 fresh caudal fin sheep ribs, using different fracture models across four randomly assigned groups (five ribs per group). The fixators were assessed using non-destructive three-point bending, torsion, and unilateral compression tests, with results averaged. Additionally, finite element analysis compared stress and strain in the polymer fixators and titanium alloy rib plates during bending and torsion tests. RESULTS: In vitro tests showed that the polymer fixators handled loads effectively up to a maximum without increase beyond a certain displacement. Bending and torsion tests via finite element analysis showed the polymer material sustained lower maximum equivalent stresses (84.455 MPa and 14.426 MPa) compared to titanium alloy plates (219.88 MPa and 46.47 MPa). CONCLUSION: The polymer rib fixator demonstrated sufficient strength for rib fracture fixation and was superior in stress management compared to titanium alloy plates in both bending and torsion tests, supporting its potential clinical application.

Metal-Organic Framework with Dual Excitation Pathways as Efficient Bifunctional Catalyst for Photo-assisted Li-O2 Batteries.

Li-O2 batteries (LOBs) have sparked significant interest due to their fascinating high theoretical energy density. However, the large overpotential for the formation and oxidation of Li2O2 during charge and discharge process seriously hinders the further development and application of LOBs. In this work, metal-organic frameworks (MOFs) with different metal clusters (Fe, Ti, Zr) are successfully synthesized, and they are employed as the photoelectrodes for the photo-assisted LOBs. The special dual excitation pathways of Fe-MOF under illumination and the superior separation efficiency of photocarriers, which significantly enhance the activation of O2/Li2O2, improving the catalytic activity of oxygen reduction reaction and oxygen evolution reaction. Moreover, compared to traditional inorganic semiconductor crystals, Fe-MOF exhibits large specific surface area and excellent O2 adsorption ability. Therefore, the LOB with Fe-MOF as the cathode exhibits large specific capacity, ultralow charge/discharge overpotential of 0.22 V at 0.05 mA cm-2 and excellent stability of 195 cycles under illumination. This study provides an environmentally friendly and highly efficient photocatalyst for LOBs, and a new strategy for designing photoelectrodes.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Primary aldosteronism with postoperative elevation of aldosterone treated effectively by finerenone: A case report.

The authors report a case of primary aldosteronism (PA) with postoperative elevation of aldosterone treated effectively by finerenone. The patient was a hypertensive man with a 30-year history of hypertension and sustained an acute myocardial infarction 5 years ago. Bilateral adrenal nodules with hyperplasia were detected and PA was confirmed. His blood potassium, direct renin concentration, and aldosterone level returned to normal after surgery of right adrenalectomy. However, 1 year after surgery, he experienced a decrease in blood potassium and an increase in aldosterone. A saline infusion test revealed an aldosterone level of 124.47 pg/mL. The patient consented to treatment with finerenone. His aldosterone and potassium levels and blood pressure have been controlled well during follow-up. This case highlights the need to screen for secondary hypertension as early as possible. Finerenone may be effective for patients with PA who are not candidates for surgery and those not relieved after surgery.

Short-term usage of proton pump inhibitors during admission was associated with increased risk of rehospitalization in critically ill patients with myocardial infarction: a cohort study.

PURPOSE: Previous studies showed that long-term use of proton pump inhibitors (PPIs) was associated with cardiovascular events. However, the impact of short-term PPI exposure on intensive care unit (ICU) patients with myocardial infarction (MI) remains largely unknown. This study aims to determine the precise correlation between short-term PPI usage during hospitalization and prognostic outcomes of ICU-admitted MI patients using Medical Information Mart for Intensive Care IV database (MIMIC-IV). METHODS: Propensity score matching (PSM) was applied to adjust confounding factors. The primary study outcome was rehospitalization with mortality and length of stay as secondary outcomes. Binary logistic, multivariable Cox, and linear regression analyses were employed to estimate the impact of short-term PPI exposure on ICU-admitted MI patients. RESULTS: A total of 7249 patients were included, involving 3628 PPI users and 3621 non-PPI users. After PSM, 2687 pairs of patients were matched. The results demonstrated a significant association between PPI exposure and increased risk of rehospitalization for MI in both univariate and multivariate [odds ratio (OR) = 1.157, 95% confidence interval (CI) 1.020-1.313] analyses through logistic regression after PSM. Furthermore, this risk was also observed in patients using PPIs > 7 days, despite decreased risk of all-cause mortality among these patients. It was also found that pantoprazole increased the risk of rehospitalization, whereas omeprazole did not. CONCLUSION: Short-term PPI usage during hospitalization was still associated with higher risk of rehospitalization for MI in ICU-admitted MI patients. Furthermore, omeprazole might be superior to pantoprazole regarding the risk of rehospitalization in ICU-admitted MI patients.

One-pot electrochemical detection of foodborne pathogen based on in situ nucleic acid amplification and wash-free assay.

A signal amplification electrochemical biosensor chip was developed to integrate loop-mediated isothermal amplification (LAMP) based on in situ nucleic acid amplification and methyl blue (MB) serving as the hybridization redox indicator for sensitive and selective foodborne pathogen detection without a washing step. The electrochemical biosensor chip was designed by a screen-printed carbon electrode modified with gold nanoparticles (Au NPs) and covered with polydimethylsiloxane membrane to form a microcell. The primers of the target were immobilized on the Au NPs by covalent attachment for in situ amplification. The electroactive MB was used as the electrochemical signal reporter and embedded into the double-stranded DNA (dsDNA) amplicons generated by LAMP. Differential pulse voltammetry was introduced to survey the dsDNA hybridization with MB, which differentiates the specifically electrode-unbound and -bound labels without a washing step. Pyrene as the back-filling agent can further improve response signaling by reducing non-specific adsorption. This method is operationally simple, specific, and effective. The biosensor showed a detection linear range of 102-107 CFU mL-1 with the limit of detection of 17.7 CFU mL-1 within 40 min. This method showed promise for on-site testing of foodborne pathogens and could be integrated into an all-in-one device.

Associations between systemic inflammation indicators and nonalcoholic fatty liver disease: evidence from a prospective study.

Background: Although inflammation has been linked to nonalcoholic fatty liver disease (NAFLD), most studies have focused only on a single indicator, leading to inconsistent results. Therefore, a large prospective study that includes a variety of well-documented single and composite indicators of inflammation is needed. This study aimed to thoroughly investigate the potential associations between different systemic inflammatory indicators and NAFLD in the UK Biobank cohort. Methods: After excluding ineligible participants, 378,139 individuals were included in the study. Associations between systemic inflammatory indicators and hepatic steatosis were assessed using multivariate logistic regression. The relationships between systemic inflammatory indicators and nonalcoholic fatty liver disease were analysed using Cox proportional hazards models, and nonlinear associations were investigated using restricted cubic splines. Results: According to the cross-sectional analysis, systemic inflammatory indicators significantly correlated with hepatic steatosis. Over a median follow-up of 13.9 years, 4,145 individuals developed NAFLD. After sufficient adjustment for confounding factors, CRP levels were found to be nonlinearly positively associated with NAFLD risk (P<0.001), representing the strongest correlation among the tested relationships; lymphocyte count and the LMR showed an L-shaped correlation; monocyte count and neutrophil count showed a linear positive correlation (all P< 0.001); and the NLR, PLR, and SII showed a U-shaped correlation (all P<0.001). Conclusions: Multiple systemic inflammatory indicators are strongly associated with the development of NAFLD, and aggressive systemic inflammation management may have a favourable impact on reducing the burden of NAFLD; further randomized controlled studies are needed.

Combined effects of heatwaves and atmospheric CO2 levels on Brassica juncea phytoremediation.

Heatwaves, expected to become more frequent, pose a significant threat to plant biomass production. This experiment was designed to estimate heatwave influence on Brassica juncea phytoremediation when superimposed on different CO2 levels. A 7-day heatwave was generated during the species flowering stage. Heatwaves decreased all B. juncea dry weights. The lowest species dry weight was recorded when the heatwave was accompanied by 250 ppm CO2, in which the biomass significantly decreased by 40.0% relative to that of no heatwave under the same atmospheric CO2 conditions. Heatwave superposition with 250 ppm CO2 reduced the Cd content in B. juncea aerial parts by 28.1% relative to that of identical environmental conditions without heatwave, whereas the opposite result was observed under 550 ppm CO2 conditions. The heatwave caused oxidative damage to B. juncea under all CO2 conditions, as manifested by increased malondialdehyde levels in the plant shoots. With heatwave superposition, antioxidant enzyme activity was enhanced by exposure to 400 and 550 ppm CO2. Considering biomass yield generation and Cd uptake capacity, heatwave superposition decreased the B. juncea phytoremediation effects, and high atmospheric CO2 conditions could alleviate detrimental effects to a certain extent. This study uniquely examines the combined effects of heatwaves and varying CO2 levels on phytoremediation, providing microscopic insights into oxidative damage and enzyme activity, highlighting the potential for CO2 enrichment to mitigate heatwave impacts, and offering comprehensive analysis for future agricultural practices and environmental management.

Optimization and component identification of ultrasound-assisted extraction of functional compounds from waste blackberry (Rubus fruticosus Pollich) seeds.

BACKGROUND: Blackberry seeds, as a by-product of processing, have potential bioactive substances and activities. A response surface method was used to determine the optimal conditions of blackberry seed extracts (BSEs) with high 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity by ultrasound-assisted extraction (UAE). The composition and antioxidant capacity of BSEs were further analyzed. RESULTS: The optimal conditions were material-to-liquid ratio of 0.07 g mL-1, ethanol concentration of 56%, extraction temperature of 39 °C and ultrasonic power of 260 W. Using these conditions, the extraction yield and total polysaccharide, phenolic and anthocyanin contents in BSEs were 0.062 g g-1 and 633.91, 36.21 and 3.07 mg g-1, respectively. The Fourier transform infrared spectra of BSEs exhibited characteristic peaks associated with polysaccharide absorption. The antioxidant capacity, DPPH and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical scavenging activity, and ferric reducing antioxidant power of BSEs were 1533.19, 1021.93 and 1093.38 mmol Trolox equivalent g-1, respectively. The delphinidin-3-O-glucoside, paeoniflorin-3-O-glucoside and cyanidin-3-O-arabinoside contents in BSEs were 3.05,12.76 and 1895.90 ± 3.45 µg g-1. Five polyphenols including gallic acid, coumaric acid, ferulic acid, catechin and caffeic acid were identified and quantified in BSEs with its contents at 8850.43, 5053.26, 4984.65, 1846.91 and 192.40 µg g-1. CONCLUSION: These results provide a method for preparing BSE containing functional components such as polysaccharides, phenols and anthocyanins through UAE, and BSEs have potential application in food industries. © 2024 Society of Chemical Industry.

Discovery of ZJCK-6-46: A Potent, Selective, and Orally Available Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase 1A Inhibitor for the Treatment of Alzheimer's Disease.

Targeting dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) has been verified to regulate the progression of tau pathology as a promising treatment for Alzheimer's disease (AD), while the research progress on DYRK1A inhibitors seemed to be in a bottleneck period. In this work, we identified 32 (ZJCK-6-46) as the most potential DYRK1A inhibitor (IC50 = 0.68 nM) through rational design, systematic structural optimization, and comprehensive evaluation. Compound 32 exhibited acceptable in vitro absorption, distribution, metabolism, and excretion (ADME) properties and significantly reduced the expression of p-Tau Thr212 in Tau (P301L) 293T cells and SH-SY5Y cells. Moreover, compound 32 showed favorable bioavailability, blood-brain barrier (BBB) permeability, and the potential of ameliorating cognitive dysfunction by obviously reducing the expression of phosphorylated tau and neuronal loss in vivo, which was deserved as a valuable molecular tool to reveal the role of DYRK1A in the pathogenesis of AD and to further promote the development of anti-AD drugs.

Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.

Purpose: We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels. Approach: PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70 keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network's denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100 keV, using a sharp vascular kernel (Bv68) and thin (0.6 mm) slice thickness (0.3 mm increment). PKAID-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy. Results: PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss. Conclusions: The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

Structural Elucidation of Glycosaminoglycans in the Tissue of Flounder and Isolation of Chondroitin Sulfate C.

Glycosaminoglycans (GAGs) are valuable bioactive polysaccharides with promising biomedical and pharmaceutical applications. In this study, we analyzed GAGs using HPLC-MS/MS from the bone (B), muscle (M), skin (S), and viscera (V) of Scophthalmus maximus (SM), Paralichthysi (P), Limanda ferruginea (LF), Cleisthenes herzensteini (G), Platichthys bicoloratus (PB), Pleuronichthys cornutus (PC), and Cleisthenes herzensteini (CH). Unsaturated disaccharide products were obtained by enzymatic hydrolysis of the GAGs and subjected to compositional analysis of chondroitin sulfate (CS), heparin sulfate (HS), and hyaluronic acid (HA), including the sulfation degree of CS and HS, as well as the content of each GAG. The contents of GAGs in the tissues and the sulfation degree differed significantly among the fish. The bone of S. maximus contained more than 12 µg of CS per mg of dry tissue. Although the fish typically contained high levels of CSA (CS-4S), some fish bone tissue exhibited elevated levels of CSC (CS-6S). The HS content was found to range from 10-150 ug/g, primarily distributed in viscera, with a predominant non-sulfated structure (HS-0S). The structure of HA is well-defined without sulfation modification. These analytical results are independent of biological classification. We provide a high-throughput rapid detection method for tissue samples using HPLC-MS/MS to rapidly screen ideal sources of GAG. On this basis, four kinds of CS were prepared and purified from flounder bone, and their molecular weight was determined to be 23-28 kDa by HPGPC-MALLS, and the disaccharide component unit was dominated by CS-6S, which is a potential substitute for CSC derived from shark cartilage.

Nanostructured High Entropy Alloys as Structural and Functional Materials.

Since their introduction in 2004, high entropy alloys (HEAs) have attracted significant attention due to their exceptional mechanical and functional properties. Advances in our understanding of atomic-scale ordering and phase formation in HEAs have facilitated the development of fabrication techniques for synthesizing nanostructured HEAs. These materials hold immense potential for applications in various fields including automobile industries, aerospace engineering, microelectronics, and clean energy, where they serve as either structural or functional materials. In this comprehensive Review, we conduct an in-depth analysis of the mechanical and functional properties of nanostructured HEAs, with a particular emphasis on the roles of different nanostructures in modulating these properties. To begin, we explore the intrinsic and extrinsic factors that influence the formation and stability of nanostructures in HEAs. Subsequently, we delve into an examination of the mechanical and electrocatalytic properties exhibited by bulk or three-dimensional (3D) nanostructured HEAs, as well as nanosized HEAs in the form of zero-dimensional (0D) nanoparticles, one-dimensional (1D) nanowires, or two-dimensional (2D) nanosheets. Finally, we present an outlook on the current research landscape, highlighting the challenges and opportunities associated with nanostructure design and the understanding of structure-property relationships in nanostructured HEAs.

Effect of Histamine H2 Receptor Antagonists on All-Cause Mortality in Critically Ill Patients With Essential Hypertension: A Retrospective Cohort Study.

Previous studies found that histamine H2 receptor antagonists (H2RAs) had blood pressure lowering and cardioprotective effects, but the impact of H2RAs on the survival outcomes of critically ill patients with essential hypertension is still unclear. The aim of this study was to investigate the association of H2RAs exposure with all-cause mortality in patients with essential hypertension based on Medical Information Mart for Intensive Care III database. A total of 17,739 patients were included, involving 8482 H2RAs users and 9257 non-H2RAs users. Propensity score matching (PSM) was performed to improve balance between 2 groups that were exposed to H2RAs or not. Kaplan-Meier survival curves were used to compare the cumulative survival rates and multivariable Cox regression models were performed to evaluate the association between H2RAs exposure and all-cause mortality. After 1:1 PSM, 4416 pairs of patients were enrolled. The results revealed potentially significant association between H2RAs exposure and decreased 30-day, 90-day, and 1-year mortalities in multivariate analyses (HR = 0.783, 95% CI: 0.696-0.882 for 30-day; HR = 0.860, 95% CI: 0.778-0.950 for 90-day; and HR = 0.883, 95% CI: 0.811-0.961 for 1-year mortality, respectively). Covariate effect analyses showed that the use of H2RAs was more beneficial in essential hypertension patients with age ≥ 60, BMI ≥ 25 kg/m2, coronary arteriosclerosis, stroke, and acute kidney failure, respectively. In conclusion, H2RAs exposure was related to lower mortalities in critically ill patients with essential hypertension, which provided novel potential strategy for the use of H2RAs in essential hypertension patients.

Immune-enhancing activity of compound polysaccharide on the inactivated influenza vaccine.

Traditional Chinese medicine polysaccharides have numerous biological activities with broad applications in the biomedical industries. However, a clear understanding of the pharmacological activities of compound polysaccharides with multi-component structures remain challenging. This study aimed to investigate the immune boosting effect of compound polysaccharides on the influenza vaccine and assess the preliminary structure-activity relationship. The compound polysaccharide (CP) was isolated from the combined Chinese herbs lentinan, pachymaran and tremellan, and purified by gradient ethanol precipitation to obtain its subcomponents of CP-20, CP-40, CP-60, and CP-80 with decreasing molecular weights. These polysaccharides were mainly composed of glucans with different linkage patterns, including α-(1 â†’ 3)-glucan, α-(1 â†’ 4)-glucan and ß-(1 â†’ 6)-glucan. A significant improvement was observed in the survival of mice vaccinated with inactivated (IAV) vaccine and the isolated polysaccharides as adjuvants. A reduction in the pulmonary virus titer and weight loss were also observed. Moreover, CP-40 and CP-60, as well as the original CP, significantly enhanced the serum anti-IAV antibody titers and interleukin IL-2, IL-5, and IL-6 concentrations. These preliminary results indicate the immune boosting effect of the compound polysaccharides is highly relevant to the specific structural properties of the subcomponent, and CP-40 is worthy of further exploration as a glycan adjuvant for the IAV vaccine.

Heavy-Atom Effect Regulating Room-Temperature Phosphorescence of Organic-Inorganic Zn-Based Halides for White-Light Emission.

Organic-inorganic metal halides (OIMHs) with room-temperature phosphorescence (RTP) properties have aroused great research enthusiasm as outstanding broadband white-light emitters. Current studies on OIMHs with white-light emission were achieved via self-trapped excitons (STEs), but the unclear mechanism of STE formation is not favorable for the design of materials. In this work, zero-dimensional OIMHs composed of organic 3,4,5-trimethoxybenzylamine (TBA) and zine halide were synthesized, which enhanced the ratio of the RTP emission to the fluorescence emission from the TBA ligand. The experimental and mechanistic analyses demonstrate that the manageable RTP is mainly caused by the heavy-atom effect. In particular, by adjusting the incorporation ratio of halogen, an obvious white-light emission with a chromaticity coordinate value of (0.31, 0.33) can be achieved. This work developed a method for regulating the RTP of OIMHs with the heavy-atom effect to realize white-light emission, providing a new idea for the design of white-light emission materials.

Predictive value of CD86 for the occurrence of sepsis (Sepsis-3) in patients with infection.

This prospective observational study explored the predictive value of CD86 in the early diagnosis of sepsis in the emergency department. The primary endpoint was the factors associated with a diagnosis of sepsis. The secondary endpoint was the factors associated with mortality among patients with sepsis. It enrolled inpatients with infection or high clinical suspicion of infection in the emergency department of a tertiary Hospital between September 2019 and June 2021. The patients were divided into the sepsis and non-sepsis groups according to the Sepsis-3 standard. The non-sepsis group included 56 patients, and the sepsis group included 65 patients (19 of whom ultimately died). The multivariable analysis showed that CD86% (odds ratio [OR] = 1.22, 95% confidence interval [CI]: 1.04-1.44, P = 0.015), platelet count (OR = 0.99, 95%CI: 0.986-0.997, P = 0.001), interleukin-10 (OR = 1.01, 95%CI: 1.004-1.025, P = 0.009), and procalcitonin (OR = 1.17, 95%CI: 1.01-1.37, P = 0.043) were independent risk factors for sepsis, while human leukocyte antigen (HLA%) (OR = 0.96, 05%CI: 0.935-0.995, P = 0.022), respiratory rate (OR = 1.16, 95%CI: 1.03-1.30, P = 0.014), and platelet count (OR = 1.01, 95%CI: 1.002-1.016, P = 0.016) were independent risk factors for death in patients with sepsis. The model for sepsis (CD86%, platelets, interleukin-10, and procalcitonin) and the model for death (HLA%, respiratory rate, and platelets) had an area under the curve (AUC) of 0.870 and 0.843, respectively. CD86% in the first 24 h after admission for acute infection was independently associated with the occurrence of sepsis in the emergency department.

Improving Stenosis Assessment in Energy Integrating Detector CT via Learned Monoenergetic Imaging Capability.

Coronary CT angiography (cCTA) is a fast non-invasive imaging exam for coronary artery disease (CAD) but struggles with dense calcifications and stents due to blooming artifacts, potentially causing stenosis overestimation. Virtual monoenergetic images (VMIs) at higher keV (e.g., 100 keV) from photon counting detector (PCD) CT have shown promise in reducing blooming artifacts and improving lumen visibility through its simultaneous high-resolution and multi-energy imaging capability. However, most cCTA exams are performed with single-energy CT (SECT) using conventional energy-integrating detectors (EID). Generating VMIs through EID-CT requires advanced multi-energy CT (MECT) scanners and potentially sacrifices temporal resolution. Given these limitations, MECT cCTA exams are not commonly performed on EID-CT and VMIs are not routinely generated. To tackle this, we aim to enhance the multi-energy imaging capability of EID-CT through the utilization of a convolutional neural network to LEarn MONoenergetic imAging from VMIs at Different Energies (LEMONADE). The neural network was trained using ten patient cCTA exams acquired on a clinical PCD-CT (NAEOTOM Alpha, Siemens Healthineers), with 70 keV VMIs as input (which is nominally equivalent to the SECT from EID-CT scanned at 120 kV) and 100 keV VMIs as the target. Subsequently, we evaluated the performance of EID-CT equipped with LEMONADE on both phantom and patient cases (n=10) for stenosis assessment. Results indicated that LEMONADE accurately quantified stenosis in three phantoms, aligning closely with ground truth and demonstrating stenosis percentage area reductions of 13%, 8%, and 9%. In patient cases, it led to a 12.9% reduction in average diameter luminal stenosis when compared to the original SECT without LEMONADE. These outcomes highlight LEMONADE's capacity to enable multi-energy CT imaging, mitigate blooming artifacts, and improve stenosis assessment for the widely available EID-CT. This has a high potential impact as most cCTA exams are performed on EID-CT.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework.

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.