Quantitative Total-Body Imaging of Blood Flow with High Temporal Resolution Early Dynamic 18 F-Fluorodeoxyglucose PET Kinetic Modeling.

Quantitative total-body PET imaging of blood flow can be performed with freely diffusible flow radiotracers such as 15 O-water and 11 C-butanol, but their short half-lives necessitate close access to a cyclotron. Past efforts to measure blood flow with the widely available radiotracer 18 F-fluorodeoxyglucose (FDG) were limited to tissues with high 18 F-FDG extraction fraction. In this study, we developed an early-dynamic 18 F-FDG PET method with high temporal resolution kinetic modeling to assess total-body blood flow based on deriving the vascular transit time of 18 F-FDG and conducted a pilot comparison study against a 11 C-butanol reference. Methods: The first two minutes of dynamic PET scans were reconstructed at high temporal resolution (60×1 s, 30×2 s) to resolve the rapid passage of the radiotracer through blood vessels. In contrast to existing methods that use blood-to-tissue transport rate (K 1 ) as a surrogate of blood flow, our method directly estimates blood flow using a distributed kinetic model (adiabatic approximation to the tissue homogeneity model; AATH). To validate our 18 F-FDG measurements of blood flow against a flow radiotracer, we analyzed total-body dynamic PET images of six human participants scanned with both 18 F-FDG and 11 C-butanol. An additional thirty-four total-body dynamic 18 F-FDG PET scans of healthy participants were analyzed for comparison against literature blood flow ranges. Regional blood flow was estimated across the body and total-body parametric imaging of blood flow was conducted for visual assessment. AATH and standard compartment model fitting was compared by the Akaike Information Criterion at different temporal resolutions. Results: 18 F-FDG blood flow was in quantitative agreement with flow measured from 11 C-butanol across same-subject regional measurements (Pearson R=0.955, p<0.001; linear regression y=0.973x-0.012), which was visually corroborated by total-body blood flow parametric imaging. Our method resolved a wide range of blood flow values across the body in broad agreement with literature ranges (e.g., healthy cohort average: 0.51±0.12 ml/min/cm 3 in the cerebral cortex and 2.03±0.64 ml/min/cm 3 in the lungs, respectively). High temporal resolution (1 to 2 s) was critical to enabling AATH modeling over standard compartment modeling. Conclusions: Total-body blood flow imaging was feasible using early-dynamic 18 F-FDG PET with high-temporal resolution kinetic modeling. Combined with standard 18 F-FDG PET methods, this method may enable efficient single-tracer flow-metabolism imaging, with numerous research and clinical applications in oncology, cardiovascular disease, pain medicine, and neuroscience.

Whole-Body Multiparametric PET in Clinical Oncology: Current Status, Challenges, and Opportunities.

The interpretation of clinical oncologic PET studies has historically used static reconstructions based on SUVs. SUVs and SUV-based images have important limitations, including dependence on uptake times and reduced conspicuity of tracer-avid lesions in organs with high background uptake. The acquisition of dynamic PET images enables additional PET reconstructions via Patlak modeling, which assumes that a tracer is irreversibly trapped by tissues of interest. The resulting multiparametric PET images capture a tracer's net trapping rate (Ki) and apparent volume of distribution (VD), separating the contributions of bound and free tracer fractions to the PET signal captured in the SUV. Potential benefits of multiparametric PET include higher quantitative stability, superior lesion conspicuity, and greater accuracy for differentiating malignant and benign lesions. However, the imaging protocols necessary for multiparametric PET are inherently more complex and time-intensive, despite the recent introduction of automated or semiautomated scanner-based reconstruction packages. In this Review, we examine the current state of multiparametric PET in whole-body oncologic imaging. We summarize the Patlak methodology and relevant tracer kinetics, discuss clinical workflows and protocol considerations, and highlight clinical challenges and opportunities. We aim to help oncologic imagers make informed decisions about whether to implement multiparametric PET in their clinical practices.

Total-Body Parametric Imaging Using Relative Patlak Plot.

Standard Patlak plot is widely used to describe FDG kinetics for dynamic PET imaging. Whole-body Patlak parametric imaging remains constrained due to the need for a full-time input function. Here, we demonstrate the Relative Patlak (RP) plot, which eliminates the need for the early-time input function, for total-body parametric imaging and its application to clinical 20-min scan acquired in list-mode. We demonstrated that the RP intercept b' is equivalent to a ratio of standardized uptake value relative to the blood, while the RP slope Ki' is equal to the standard Patlak Ki multiplied by a global scaling factor for each subject. One challenge in applying RP to a short scan duration (20 min) is the high noise in parametric images. We applied a deep kernel method for noise reduction. Using the standard Patlak plot as the reference, the RP method was evaluated for lesion quantification, lesion-to-background contrast, and myocardial visualization in total-body parametric imaging with uEXPLORER in 22 human subjects who underwent a 1-h dynamic 18F-FDG scan. The RP method was also applied to the dynamic data regenerated from a clinical standard 20-min scan either at 1-h or 2-h post-injection for two cancer patients. We demonstrated that it is feasible to obtain high-quality parametric images from 20-min dynamic scans using the RP plot with a self-supervised deep-kernel noise reduction strategy. The RP Ki' highly correlated with Ki in lesions and major organs, demonstrating its quantitative potential across subjects. Compared to conventional SUVs, the Ki' images significantly improved lesion contrast and enabled visualization of the myocardium for potential cardiac assessment. The application of RP parametric imaging to two clinical scans also showed similar benefits. Total-body PET with the RP plot is feasible to generate parametric images from the dynamic data of a 20-min clinical scan.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

The role of brown adipose tissue in branched-chain amino acid clearance in people.

Brown adipose tissue (BAT) in rodents appears to be an important tissue for the clearance of plasma branched-chain amino acids (BCAAs) contributing to improved metabolic health. However, the role of human BAT in plasma BCAA clearance is poorly understood. Here, we evaluate patients with prostate cancer who underwent positron emission tomography-computed tomography imaging after an injection of 18F-fluciclovine (L-leucine analog). Supraclavicular adipose tissue (AT; primary location of human BAT) has a higher net uptake rate for 18F-fluciclovine compared to subcutaneous abdominal and upper chest AT. Supraclavicular AT 18F-fluciclovine net uptake rate is lower in patients with obesity and type 2 diabetes. Finally, the expression of genes involved in BCAA catabolism is higher in the supraclavicular AT of healthy people with high BAT volume compared to those with low BAT volume. These findings support the notion that BAT can potentially function as a metabolic sink for plasma BCAA clearance in people.

Dynamically visualizing profibrotic maladaptive repair after acute kidney injury by fibroblast activation protein imaging.

A major challenge in prevention and early treatment of organ fibrosis is the lack of valuable tools to assess the evolving profibrotic maladaptive repair after injury in vivo in a non-invasive way. Here, using acute kidney injury (AKI) as an example, we tested the utility of fibroblast activation protein (FAP) imaging for dynamic assessment of maladaptive repair after injury. The temporospatial pattern of kidney FAP expression after injury was first characterized. Single-cell RNA sequencing and immunostaining analysis of patient biopsies were combined to show that FAP was specifically upregulated in kidney fibroblasts after AKI and was associated with fibroblast activation and chronic kidney disease (CKD) progression. This was corroborated in AKI mouse models, where a sustained and exaggerated kidney FAP upregulation was coupled to persistent fibroblast activation and a fibrotic outcome, linking kidney FAP level to post-insult maladaptive repair. Furthermore, using positron emission tomography (PET)/CT scanning with FAP-inhibitor tracers ([18F]FAPI-42, [18F]FAPT) targeting FAP, we demonstrated the feasibility of non-invasively tracking of maladaptive repair evolution toward kidney fibrosis. Importantly, a sustained increase in kidney [18F]FAPT (less hepatobiliary metabolized than [18F]FAPI-42) uptake reflected persistent kidney upregulation of FAP and characterized maladaptive repair after AKI. Kidney [18F]FAPT uptake at hour 2-day 7 correlated with kidney fibrosis 14 days after AKI. Similar changes in [18F]FAPI-42 PET/CT imaging were observed in patients with AKI and CKD progression. Thus, persistent kidney FAP upregulation after AKI was associated with maladaptive repair and a fibrotic outcome. Hence, FAP-specific PET/CT imaging enables dynamic visualization of maladaptive repair after AKI and prediction of kidney fibrosis within a clinically actionable window.

Quantitative PET imaging and modeling of molecular blood-brain barrier permeability.

Blood-brain barrier (BBB) disruption is involved in the pathogenesis and progression of many neurological and systemic diseases. Non-invasive assessment of BBB permeability in humans has mainly been performed with dynamic contrast-enhanced magnetic resonance imaging, evaluating the BBB as a structural barrier. Here, we developed a novel non-invasive positron emission tomography (PET) method in humans to measure the BBB permeability of molecular radiotracers that cross the BBB through different transport mechanisms. Our method uses high-temporal resolution dynamic imaging and kinetic modeling to jointly estimate cerebral blood flow and tracer-specific BBB transport rate from a single dynamic PET scan and measure the molecular permeability-surface area (PS) product of the radiotracer. We show our method can resolve BBB PS across three PET radiotracers with greatly differing permeabilities, measure reductions in BBB PS of 18F-fluorodeoxyglucose (FDG) in healthy aging, and demonstrate a possible brain-body association between decreased FDG BBB PS in patients with metabolic dysfunction-associated steatotic liver inflammation. Our method opens new directions to efficiently study the molecular permeability of the human BBB in vivo using the large catalogue of available molecular PET tracers.

Kinetic modeling of 18 F-PI-2620 binding in the brain using an image-derived input function with total-body PET.

Accurate quantification of tau binding from 18 F-PI-2620 PET requires kinetic modeling and an input function. Here, we implemented a non-invasive Image-derived input function (IDIF) derived using the state-of-the-art total-body uEXPLORER PET/CT scanner to quantify tau binding and tracer delivery rate from 18 F-PI-2620 in the brain. Additionally, we explored the impact of scan duration on the quantification of kinetic parameters. Total-body PET dynamic data from 15 elderly participants were acquired. Time-activity curves from the grey matter regions of interest (ROIs) were fitted to the two-tissue compartmental model (2TCM) using a subject-specific IDIF derived from the descending aorta. ROI-specific kinetic parameters were estimated for different scan durations ranging from 10 to 90 minutes. Logan graphical analysis was also used to estimate the total distribution volume (V T ). Differences in kinetic parameters were observed between ROIs, including significant reduction in tracer delivery rate (K 1 ) in the medial temporal lobe. All kinetic parameters remained relatively stable after the 60-minute scan window across all ROIs, with K 1 showing high stability after 30 minutes of scan duration. Excellent correlation was observed between V T estimated using 2TCM and Logan plot analysis. This study demonstrated the utility of IDIF with total-body PET in investigating 18 F-PI-2620 kinetics in the brain.

Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging.

Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method allows DECT imaging on PET/CT without requiring a second X-ray CT scan. It combines the already existing X-ray CT image with a 511 keV γ -ray CT (gCT) image reconstructed from time-of-flight PET emission data. A kernelized framework has been developed for reconstructing gCT image but this method has not fully exploited the potential of prior knowledge. Use of deep neural networks may explore the power of deep learning in this application. However, common approaches require a large database for training, which is impractical for a new imaging method like PET-enabled DECT. Here, we propose a single-subject method by using neural-network representation as a deep coefficient prior to improving gCT image reconstruction without population-based pre-training. The resulting optimization problem becomes the tomographic estimation of nonlinear neural-network parameters from gCT projection data. This complicated problem can be efficiently solved by utilizing the optimization transfer strategy with quadratic surrogates. Each iteration of the proposed neural optimization transfer algorithm includes: PET activity image update; gCT image update; and least-square neural-network learning in the gCT image domain. This algorithm is guaranteed to monotonically increase the data likelihood. Results from computer simulation, real phantom data and real patient data have demonstrated that the proposed method can significantly improve gCT image quality and consequent multi-material decomposition as compared to other methods.

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging.

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation. METHODS: The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. RESULTS: The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method. CONCLUSION: The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Longer serum phosphorus time in range associated with lower mortality risk among peritoneal dialysis patients: a multicenter retrospective cohort study.

BACKGROUND: Relationship between serum phosphorus time in range and mortality risk in peritoneal dialysis (PD) patients remains uncertain. We aimed to evaluate the association between serum phosphorus time in range and all-cause mortality in Chinese PD population. METHODS: This was a multicenter, retrospective, cohort study of 1,915 patients collected from January 2008 to October 2020 in 4 Chinese centers. Serum phosphorus time in range was estimated as the months during the first year that a patient's serum phosphorus level was within the target range (defined as 1.13-1.78 mmol/L). The primary outcome was all-cause mortality. The secondary outcomes were cardiovascular (CV) mortality and PD withdrawal. Cox proportional hazards regression model with comprehensive adjustments was used to assess the association. RESULTS: The primary outcome occurred in 249 (13.0%) PD patients over a median follow-up of 28 months. Overall, the serum phosphorus time in range was negatively associated with all-cause mortality (per 3-month increments, adjusted HR [aHR], 0.83; 95%CI: 0.75-0.92), CV mortality (per 3-month increments, aHR, 0.87; 95%CI: 0.77-0.99), and PD withdrawal (per 3-month increments, aHR, 0.89; 95%CI: 0.83-0.95). Competing-risk model showed that the relationship of serum phosphorus time in range with all-cause mortality remained stable. None of the variables including demographics, history of diabetes and CV disease, as well as several PD-related and clinical indicators modified this association. CONCLUSIONS: PD patients with longer serum phosphorus time in range in the first year was negatively associated with all-cause mortality and CV mortality. Our findings highlight the importance of maintaining serum phosphorus levels within 1.13-1.78 mmol/L for PD patients.

High-Temporal-Resolution Kinetic Modeling of Lung Tumors with Dual-Blood Input Function Using Total-Body Dynamic PET.

The lungs are supplied by both the pulmonary arteries carrying deoxygenated blood originating from the right ventricle and the bronchial arteries carrying oxygenated blood downstream from the left ventricle. However, this effect of dual blood supply has never been investigated using PET, partially because the temporal resolution of conventional dynamic PET scans is limited. The advent of PET scanners with a long axial field of view, such as the uEXPLORER total-body PET/CT system, permits dynamic imaging with high temporal resolution (HTR). In this work, we modeled the dual-blood input function (DBIF) and studied its impact on the kinetic quantification of normal lung tissue and lung tumors using HTR dynamic PET imaging. Methods: Thirteen healthy subjects and 6 cancer subjects with lung tumors underwent a dynamic 18F-FDG scan with the uEXPLORER for 1 h. Data were reconstructed into dynamic frames of 1 s in the early phase. Regional time-activity curves of lung tissue and tumors were analyzed using a 2-tissue compartmental model with 3 different input functions: the right ventricle input function, left ventricle input function, and proposed DBIF, all with time delay and dispersion corrections. These models were compared for time-activity curve fitting quality using the corrected Akaike information criterion and for differentiating lung tumors from lung tissue using the Mann-Whitney U test. Voxelwise multiparametric images by the DBIF model were further generated to verify the regional kinetic analysis. Results: The effect of dual blood supply was pronounced in the high-temporal-resolution time-activity curves of lung tumors. The DBIF model achieved better time-activity curve fitting than the other 2 single-input models according to the corrected Akaike information criterion. The estimated fraction of left ventricle input was low in normal lung tissue of healthy subjects but much higher in lung tumors (∼0.04 vs. ∼0.3, P < 0.0003). The DBIF model also showed better robustness in the difference in 18F-FDG net influx rate [Formula: see text] and delivery rate [Formula: see text] between lung tumors and normal lung tissue. Multiparametric imaging with the DBIF model further confirmed the differences in tracer kinetics between normal lung tissue and lung tumors. Conclusion: The effect of dual blood supply in the lungs was demonstrated using HTR dynamic imaging and compartmental modeling with the proposed DBIF model. The effect was small in lung tissue but nonnegligible in lung tumors. HTR dynamic imaging with total-body PET can offer a sensitive tool for investigating lung diseases.

Systematic Review on Learning-based Spectral CT.

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Super-resolution reconstruction of γ-ray CT images for PET-enabled dual-energy CT imaging.

Dual-energy computed tomography (DECT) enables material decomposition for tissues and produces additional information for PET/CT imaging to potentially improve the characterization of diseases. PET-enabled DECT (PDECT) allows the generation of PET and DECT images simultaneously with a conventional PET/CT scanner without the need for a second x-ray CT scan. In PDECT, high-energy γ-ray CT (GCT) images at 511 keV are obtained from time-of-flight (TOF) PET data and are combined with the existing x-ray CT images to form DECT imaging. We have developed a kernel-based maximum-likelihood attenuation and activity (MLAA) method that uses x-ray CT images as a priori information for noise suppression. However, our previous studies focused on GCT image reconstruction at the PET image resolution which is coarser than the image resolution of the x-ray CT. In this work, we explored the feasibility of generating super-resolution GCT images at the corresponding CT resolution. The study was conducted using both phantom and patient scans acquired with the uEXPLORER total-body PET/CT system. GCT images at the PET resolution with a pixel size of 4.0 mm × 4.0 mm and at the CT resolution with a pixel size of 1.2 mm × 1.2 mm were reconstructed using both the standard MLAA and kernel MLAA methods. The results indicated that the GCT images at the CT resolution had sharper edges and revealed more structural details compared to the images reconstructed at the PET resolution. Furthermore, images from the kernel MLAA method showed substantially improved image quality compared to those obtained with the standard MLAA method.

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and patient results.

X-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging, but would either require hardware upgrade or increase radiation dose due to the added second x-ray CT scan. Recently proposed PET-enabled DECT method allows dual-energy spectral imaging using a conventional PET/CT scanner without the need for a second x-ray CT scan. A gamma-ray CT (gCT) image at 511 keV can be generated from the existing time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and is then combined with the low-energy x-ray CT image to form dual-energy spectral imaging. To improve the image quality of gCT, a kernel MLAA method was further proposed by incorporating x-ray CT as a priori information. The concept of this PET-enabled DECT has been validated using simulation studies, but not yet with 3D real data. In this work, we developed a general open-source implementation for gCT reconstruction from PET data and use this implementation for the first real data validation with both a physical phantom study and a human subject study on a uEXPLORER total-body PET/CT system. These results have demonstrated the feasibility of this method for spectral imaging and material decomposition.

Biogenic manganese oxides promote metal(loid) remediation by shaping microbial communities in biological aqua crust.

Biological aqua crust (biogenic aqua crust-BAC) is a potentially sustainable solution for metal(loid) bioremediation in global water using solar energy. However, the key geochemical factors and underlying mechanisms shaping microbial communities in BAC remain poorly understood. The current study aimed at determining the in situ metal(loid) distribution and the key geochemical factors related to microbial community structure and metal(loid)-related genes in BAC of a representative Pb/Zn tailing pond. Here we showed that abundant metal(loid)s (e.g. Pb, As) were co-distributed with Mn/Fe-rich minerals (e.g. biogenic Mn oxide, FeOOH) in BAC. Biogenic Mn oxide (i.e. Mn) was the most dominant factor in shaping microbial community structure in BAC and source tailings. Along with the fact that keystone species (e.g. Burkholderiales, Haliscomenobacter) have the potential to promote Mn ion oxidization and particle agglomeration, as well as Mn is highly associated with metal(loid)-related genes, especially genes related to As redox (e.g. arsC, aoxA), and Cd transport (e.g. zipB), biogenic Mn oxides thus effectively enhance metal(loid) remediation by accelerating the formation of organo-mineral aggregates in biofilm-rich BAC system. Our study indicated that biogenic Mn oxides may play essential roles in facilitating in situ metal(loid) bioremediation in BAC of mine drainage.

A Cross-Sectional Survey on Pain Management in Dermal Filler Injections from Physicians' and Patients' Perspectives.

BACKGROUND: Hyaluronic acid (HA) dermal fillers injection is a common procedure in patients with cosmetic needs. Concomitant pain is a major complaint among patients undergoing HA filler injections. Relevant research is limited and there is no consensus on pain management of dermal filler injection. OBJECTIVES: To assist physicians in determining a more appropriate treatment approach, and to better provide treatment suggestions. METHODS: A nationwide (China) cross-sectional survey was conducted using questionnaires designed for physicians and patients, respectively. A total of 62 semi-structured questionnaires were administered to aesthetic physicians via face-to-face interview, whereas 123 online-based questionnaires were collected from patients who have ever undergone HA treatment. The collected questionnaire information was analyzed using descriptive statistics and content analysis. RESULTS: 42 (67.74%) physicians observed that over 50% of their patients were concerned about pain during injection. 101 (82.11%) of patients were concerned about impending pain ≥5 points (a total score is 10) before injection. For preferred pain relief modalities, 48 (77.42%) physicians would choose a hyaluronic acid dermal filler with lidocaine, and 82 (66.67%) patients would choose anesthetic-containing products. 59 (95.16%) physicians who injected lidocaine-containing hyaluronic acid found patients had a comfortable treatment experience. CONCLUSIONS: Pain management during hyaluronic acid dermal fillers injection is important from both perspectives of physicians and patients. This survey showed that compared with other analgesic methods, lidocaine-containing hyaluronic acid has offered a more satisfying experience. It also provides insights to physicians and patients in pain management. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Systematic Review on Learning-based Spectral CT.

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study.

BACKGROUND: There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images. METHODS: The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared. RESULTS: In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98). CONCLUSIONS: The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.

Positive association between dietary manganese intake and new-onset hypertension: A nationwide cohort study in China.

BACKGROUND AND AIM: To date, few studies have investigated the association between dietary manganese intake and the risk of hypertension, so the prospective relationship of dietary manganese intake and new-onset hypertension remains uncertain. We aimed to investigate the association between dietary manganese intake and the risk of new-onset hypertension in the general Chinese population. METHODS AND RESULTS: This prospective cohort study included 12,177 participants who were free of hypertension at baseline from China Health and Nutrition Survey (CHNS). Dietary intake was measured by 3 consecutive 24-h dietary recalls combined with a household food inventory. The study outcome was new-onset hypertension, defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or diagnosed by a physician or under antihypertensive treatment during the follow-up. During a median follow-up duration of 6.1 years, 4269 (44.9 per 1000 person-years) participants developed new-onset hypertension. Overall, there was a positive association between dietary manganese intake and new-onset hypertension. The adjusted HRs (95%CIs) of new-onset hypertension were 1.00 (reference), 0.97 (0.87, 1.08), 1.24 (1.10, 1.39) and 1.75 (1.52, 2.01) across the quartiles of dietary manganese intake, respectively. Accordingly, a significantly higher risk of new-onset hypertension (HR, 1.38; 95%CI: 1.27, 1.50) was found in participants in quartiles 3-4 of dietary manganese intake (≥6.0 mg/day), compared with those in quartiles 1-2 (<6.0 mg/day). CONCLUSIONS: In the general Chinese population, dietary manganese intake was positively associated with the risk of new hypertension, independent of sodium intake and other important covariates.