The Role of Total-Body PET in Drug Development and Evaluation: Status and Outlook.

Total-body PET, an emerging technique, enables high-quality simultaneous total-body dynamic PET acquisition and accurate kinetic analysis. It has the potential to facilitate the study of multiple tracers while minimizing radiation dose and improving tracer-specific imaging. This advancement holds promise for enhancing the development and clinical evaluation of drugs, particularly radiopharmaceuticals. Multiple clinical trials are using a total-body PET scanner to explore existing and innovative radiopharmaceuticals. However, challenges persist, along with the opportunities, with regard to the use of total-body PET in drug development and evaluation. Specifically, considerations relate to the role of total-body PET in clinical pharmacologic evaluations and its integration into the theranostic paradigm. In this review, state-of-the-art total-body PET and its potential roles in pharmaceutical research are explored.

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma.

Background: Patients with lymphoma receive multiple positron emission tomography/computed tomography (PET/CT) exams for monitoring of the therapeutic response. With PET imaging, a reduced level of injected fluorine-18 fluorodeoxyglucose ([18F]FDG) activity can be administered while maintaining the image quality. In this study, we investigated the efficacy of applying a deep learning (DL) denoising-technique on image quality and the quantification of metabolic parameters and Deauville score (DS) of a low [18F]FDG dose PET in patients with lymphoma. Methods: This study retrospectively enrolled 62 patients who underwent [18F]FDG PET scans. The low-dose (LD) data were simulated by taking a 50% duration of routine-dose (RD) PET list-mode data in the reconstruction, and a U-Net-based denoising neural network was applied to improve the images of LD PET. The visual image quality score (1 = undiagnostic, 5 = excellent) and DS were assessed in all patients by nuclear radiologists. The maximum, mean, and standard deviation (SD) of the standardized uptake value (SUV) in the liver and mediastinum were measured. In addition, lesions in some patients were segmented using a fixed threshold of 2.5, and their SUV, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) were measured. The correlation coefficient and limits of agreement between the RD and LD group were analyzed. Results: The visual image quality of the LD group was improved compared with the RD group. The DS was similar between the RD and LD group, and the negative (DS 1-3) and positive (DS 4-5) results remained unchanged. The correlation coefficients of SUV in the liver, mediastinum, and lesions were all >0.85. The mean differences of SUVmax and SUVmean between the RD and LD groups, respectively, were 0.22 [95% confidence interval (CI): -0.19 to 0.64] and 0.02 (95% CI: -0.17 to 0.20) in the liver, 0.13 (95% CI: -0.17 to 0.42) and 0.02 (95% CI: -0.12 to 0.16) in the mediastinum, and -0.75 (95% CI: -3.42 to 1.91), and -0.13 (95% CI: -0.57 to 0.31) in lesions. The mean differences in MTV and TLG were 0.85 (95% CI: -2.27 to 3.98) and 4.06 (95% CI: -20.53 to 28.64) between the RD and LD groups. Conclusions: The DL denoising technique enables accurate tumor assessment and quantification with LD [18F]FDG PET imaging in patients with lymphoma.

All-trans retinoic acid acts as a dual-purpose inhibitor of SARS-CoV-2 infection and inflammation.

Coronavirus disease 2019 (COVID-19) was an epidemic that effected human health caused by SARS-CoV-2 infection. All-trans retinoic acid (ATRA) has anti-inflammatory capability. In this article, we evaluated the effectiveness and revealed the molecular mechanism of ATRA for treating SARS-CoV-2 using deep learning, in vitro studies, multi-scale molecular modeling, and network pharmacology. The DeepDTA model suggested that ATRA would be effective against COVID-19. In vitro studies confirmed the antiviral activity of ATRA. Subsequently, multi-scale molecular modeling indicated that ATRA could binding to angiotensin converting enzyme 2 (ACE2), 3C-like protease (3CLpro), RNA dependent RNA polymerase (RdRp), helicase, and 3'-to-5' exonuclease by non-covalent interactions. Additionally, network pharmacology suggested that ATRA alleviated inflammatory response by regulating the IL-17 signaling pathway and binding with TNF, PTGS2, and MAPK1 directly. In summary, our findings provide the first evidence that ATRA suppresses the entry and replication of SARS-CoV-2, and regulates inflammatory response of host cells.

CPSF6 regulates alternative polyadenylation and proliferation of cancer cells through phase separation.

Cancer cells usually exhibit shortened 3' untranslated regions (UTRs) due to alternative polyadenylation (APA) to promote cell proliferation and migration. Upregulated CPSF6 leads to a systematic prolongation of 3' UTRs, but CPSF6 expression in tumors is typically higher than that in healthy tissues. This contradictory observation suggests that it is necessary to investigate the underlying mechanism by which CPSF6 regulates APA switching in cancer. Here, we find that CPSF6 can undergo liquid-liquid phase separation (LLPS), and elevated LLPS is associated with the preferential usage of the distal poly(A) sites. CLK2, a kinase upregulated in cancer cells, destructs CPSF6 LLPS by phosphorylating its arginine/serine-like domain. The reduction of CPSF6 LLPS can lead to a shortened 3' UTR of cell-cycle-related genes and accelerate cell proliferation. These results suggest that CPSF6 LLPS, rather than its expression level, may be responsible for APA regulation in cancer cells.

11ß-Hydroxysteroid Dehydrogenase Type 1 Facilitates Osteoporosis by Turning on Osteoclastogenesis through Hippo Signaling.

11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1) is a key enzyme that transform cortisone to cortisol, which activates the endogenous glucocorticoid function. 11ß-HSD1 has been observed to regulate skeletal metabolism, specifically within osteoblasts. However, the function of 11ß-HSD1 in osteoclasts has not been elucidated. In this study, we observed increased 11ß-HSD1 expression in osteoclasts within an osteoporotic mice model (ovariectomized mice). Then, 11ß-HSD1 global knock-out or knock-in mice were employed to demonstrate its function in manipulating bone metabolism, showing significant bone volume decrease in 11ß-HSD1 knock-in mice. Furthermore, specifically knock out 11ß-HSD1 in osteoclasts, by crossing cathepsin-cre mice with 11ß-HSD1flox/flox mice, presented significant protecting effect of skeleton when they underwent ovariectomy surgery. In vitro experiments showed the endogenous high expression of 11ß-HSD1 lead to osteoclast formation and maturation. Meanwhile, we found 11ß-HSD1 facilitated mature osteoclasts formation inhibited bone formation coupled H type vessel (CD31hiEmcnhi) growth through reduction of PDFG-BB secretion. Finally, transcriptome sequencing of 11ß-HSD1 knock in osteoclast progenitor cells indicated the Hippo pathway1 was mostly enriched. Then, by suppression of YAP expression in Hippo signaling, we observed the redundant of osteoclasts formation even in 11ß-HSD1 high expression conditions. In conclusion, our study demonstrated the role of 11ß-HSD1 in facilitating osteoclasts formation and maturation through the Hippo signaling, which is a new therapeutic target to manage osteoporosis.

Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module.

Motor imagery (MI) is a popular paradigm for controlling electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to accurately classify MI-related EEG activity. Recently, the development of deep learning has begun to draw increasing attention in the BCI research community because it does not need to use sophisticated signal preprocessing and can automatically extract features. In this paper, we propose a deep learning model for use in MI-based BCI systems. Our model makes use of a convolutional neural network based on a multi-scale and channel-temporal attention module (CTAM), which called MSCTANN. The multi-scale module is able to extract a large number of features, while the attention module includes both a channel attention module and a temporal attention module, which together allow the model to focus attention on the most important features extracted from the data. The multi-scale module and the attention module are connected by a residual module, which avoids the degradation of the network. Our network model is built from these three core modules, which combine to improve the recognition ability of the network for EEG signals. Our experimental results on three datasets (BCI competition IV 2a, III IIIa and IV 1) show that our proposed method has better performance than other state-of-the-art methods, with accuracy rates of 80.6%, 83.56% and 79.84%. Our model has stable performance in decoding EEG signals and achieves efficient classification performance while using fewer network parameters than other comparable state-of-the-art methods.

Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection.

Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving reconstruction-based method and proposes a novel O mni-frequency C hannel-selection R econstruction (OCR-GAN) network to handle sensory anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-the-art 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.1 ↑ and the current SOTA method by +0.3 ↑ . The source code is available in the additional materials.

Recent advances of long non-coding RNAs in control of hepatic gluconeogenesis.

Gluconeogenesis is the main process for endogenous glucose production during prolonged fasting, or certain pathological conditions, which occurs primarily in the liver. Hepatic gluconeogenesis is a biochemical process that is finely controlled by hormones such as insulin and glucagon, and it is of great importance for maintaining normal physiological blood glucose levels. Dysregulated gluconeogenesis induced by obesity is often associated with hyperglycemia, hyperinsulinemia, and type 2 diabetes (T2D). Long noncoding RNAs (lncRNAs) are involved in various cellular events, from gene transcription to protein translation, stability, and function. In recent years, a growing number of evidences has shown that lncRNAs play a key role in hepatic gluconeogenesis and thereby, affect the pathogenesis of T2D. Here we summarized the recent progress in lncRNAs and hepatic gluconeogenesis.

Study of Resource Allocation for 5G URLLC/eMBB-Oriented Power Hybrid Service.

With the rapid development of the 5G power Internet of Things (IoT), new power systems have higher requirements for data transmission rates, latency, reliability, and energy efficiency. Specifically, the hybrid service of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) has brought new challenges to the differentiated service of the 5G power IoT. To solve the above problems, this paper first constructs a power IoT model based on NOMA for the mixed service of URLLC and eMBB. Considering the shortage of resource utilization in eMBB and URLLC hybrid power service scenarios, the problem of maximizing system throughput through joint channel selection and power allocation is proposed. The channel selection algorithm based on matching as well as the power allocation algorithm based on water injection are developed to tackle the problem. Both theoretical analysis and experimental simulation verify that our method has superior performance in system throughput and spectrum efficiency.

Evaluation of a respiratory motion-corrected image reconstruction algorithm in 2-[18F]FDG and [68Ga]Ga-DOTA-NOC PET/CT: impacts on image quality and tumor quantification.

Background: Respiratory motions may cause artifacts on positron emission tomography (PET) images that degrade image quality and quantification accuracy. This study aimed to evaluate the effect of a respiratory motion-corrected image reconstruction (MCIR) algorithm on image quality and tumor quantification compared with nongated/nonmotion-corrected reconstruction. Methods: We used a phantom consisting of 5 motion spheres immersed in a chamber driven by a motor. The spheres and the background chamber were filled with 18F solution at a sphere-to-background ratio of 5:1. We enrolled 42 and 16 patients undergoing 2-deoxy-2-[18F]fluoro-D-glucose {2-[18F]FDG} and 68Ga-labeled [1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid]-1-Nal3-octreotide {[68Ga]Ga-DOTA-NOC} PET/computed tomography (CT) from whom 74 and 30 lesions were segmented, respectively. Three reconstructions were performed: data-driven gating-based motion correction (DDGMC), external vital signal module-based motion correction (VSMMC), and noncorrection reconstruction. The standardized uptake values (SUVs) and the volume of the spheres and the lesions were measured and compared among the 3 reconstruction groups. The image noise in the liver was measured, and the visual image quality of motion artifacts was scored by radiologists in the patient study. Results: In the phantom study, the spheres' SUVs increased by 26-36%, and the volumes decreased by 35-38% in DDGMC and VSMMC compared with the noncorrection group. In the 2-[18F]FDG PET patient study, the lesions' SUVs had a median increase of 10.87-12.65% while the volumes had a median decrease of 14.88-15.18% in DDGMC and VSMMC compared with those of noncorrection. In the [68Ga]Ga-DOTA-NOC PET patient study, the lesions' SUVs increased by 14.23-15.45%, and the volumes decreased by 19.11-20.94% in DDGMC and VSMMC. The image noise in the liver was equal between the DDGMC, VSMMC, and noncorrection groups. Radiologists found improved image quality in more than 45% of the cases in DDGMC and VSMMC compared with the noncorrection group. There was no statistically significant difference in SUVs, volumes, or visual image quality scores between DDGMC and VSMMC. Conclusions: MCIR improves tumor quantification accuracy and visual image quality by reducing respiratory motion artifacts without compromised image noise performance or elongated acquisition time in 2-[18F]FDG and [68Ga]Ga-DOTA-NOC PET/CT tumor imaging. The performance of DDG-driven MCIR is as good as that of the external device-driven solution.

Exosomes from Inflamed Macrophages Promote the Progression of Parkinson's Disease by Inducing Neuroinflammation.

Inflammation is a common feature both for Parkinson's disease (PD) and obesity-associated metabolic syndromes. Inflammation mediated by inflamed macrophages in white adipose tissue plays a pivotal role for the pathogenesis of metabolic syndromes. Exosomes are important carriers connecting peripheral tissues and the central nervous system (CNS). Therefore, we speculate that exosomes derived from inflamed macrophages may be involved in the pathological progression of PD. Here, we prepared exosomes from lipopolysaccharide (LPS) or interferon gamma (IFNγ) treated macrophages (inflamed macrophages) and examined their potential roles in PD. Our data showed that exosomes from inflamed macrophages stimulate proinflammatory cytokine expression in primary microglia and astrocytes. In vivo, inflamed macrophage exosomes induce behavioral defects in mice as evidenced by shortened duration in the rotarod test and prolonged latency in the pole test. The treatment of exosomes also reduces tyrosine hydroxylase (TH) positive cells in the substantia nigra pars compacta (SNpc) and striatum. All these PD-like phenotypes are likely due to the activation of microglia and astrocytes induced by exosomes from inflamed macrophages. Exosome sequencing, together with bioinformatics analysis and functional studies, revealed that exosomal miRNAs such as miR-155-5p are likely a key factor for inducing an inflammatory response in glial cells. These results indicate that exosomes derived from inflamed macrophages are likely a causative factor for developing PD. In this regard, inflamed macrophage exosomes might be a linker transducing the peripheral tissue inflammation into the CNS.

Nuclear m6 A reader YTHDC1 suppresses proximal alternative polyadenylation sites by interfering with the 3' processing machinery.

N6-methyladenosine (m6 A) and alternative polyadenylation (APA) are important regulators of gene expression in eukaryotes. Recently, it was found that m6 A is closely related to APA. However, the molecular mechanism of this new APA regulation remains elusive. Here, we show that YTHDC1, a nuclear m6 A reader, can suppress proximal APA sites and produce longer 3' UTR transcripts by binding to their upstream m6 A sites. YTHDC1 can directly interact with the 3' end processing factor FIP1L1 and interfere with its ability to recruit CPSF4. Binding to the m6 A sites can promote liquid-liquid phase separation of YTHDC1 and FIP1L1, which may play an important role in their interaction and APA regulation. Collectively, YTHDC1 as an m6 A "reader" links m6 A modification with pre-mRNA 3' end processing, providing a new mechanism for APA regulation.

A glycogen storage disease type 1a patient with type 2 diabetes.

BACKGROUND: Glycogen storage disease type 1a (GSD1a) is an inborn genetic disease caused by glucose-6-phosphatase-α (G6Pase-α) deficiency and is often observed to lead to endogenous glucose production disorders manifesting as hypoglycemia, hyperuricemia, hyperlipidemia, lactic acidemia, hepatomegaly, and nephromegaly. The development of GSD1a with diabetes is relatively rare, and the underlying pathogenesis remains unclear. CASE PRESENTATION: Here we describe a case of a 25-year-old Chinese female patient with GSD1a, who developed uncontrolled type 2 diabetes mellitus (T2DM) as a young adult. The patient was diagnosed with GSD1a disease at the age of 10 and was subsequently treated with an uncooked cornstarch diet. Recently, the patient was treated in our hospital for vomiting and electrolyte imbalance and was subsequently diagnosed with T2DM. Owing to the impaired secretory function of the patient's pancreatic islets, liver dysfunction, hypothyroidism, severe hyperlipidemia, and huge hepatic adenoma, we adopted diet control, insulin therapy, and hepatic adenoma resection to alleviate this situation. The WES discovered compound heterozygous mutations at the exon 5 of G6PC gene at 17th chromosome in the patient, c.648G>T (p.L216 L, NM_000151.4, rs80356484) in her father and c.674T>C (p.L225 P, NM_000151.4, rs1555560128) in her mother. c.648G>T is a well-known splice-site mutation, which causes CTG changing to CTT at protein 216 and creates a new splicing site 91 bp downstream of the authentic splice site, though both codons encode leucine. c.674T>C is a known missense mutation that causes TGC to become CGC at protein 225, thereby changing from coding for leucine to coding for proline. CONCLUSION: We report a rare case of GSD1a with T2DM. On the basis of the pathogenesis of GSD1a, we recommend attentiveness to possible development of fasting hypoglycemia caused by GSD and postprandial hyperglycemia from diabetes. As the disease is better identified and treated, and as patients with GSD live longer, this challenge may appear more frequently. Therefore, it is necessary to have a deeper and more comprehensive understanding of the pathophysiology of the disease and explore suitable treatment options.

Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT.

With the deepening of population aging in China, chronic diseases are a major public health concern that threatens the life and health of nationals. Mobile health or mHealth can effectively monitor chronic diseases, which holds vital significance to the alleviation of social pressure caused by aging. To patients with chronic diseases, mHealth cannot give full play to its value, only when it is used in the long term. However, there is not yet research exploring mHealth continuance intention from the perspective of elders with chronic diseases. So, this research represents the first attempt to empirically analyze mHealth continuance intention from the perspective of elders with chronic diseases. The purpose of this research is to make up the research gap of the mHealth field and to put forward theoretical and practical implications based on research results. To obtain research data, a questionnaire was conducted. A total of 926 copies were collected online and 527 copies were collected offline. The structural equation model (SEM) was used for data analysis. Research results suggest that confirmation can significantly influence satisfaction, performance expectancy and effort expectancy. Meanwhile, confirmation and performance expectancy can significantly influence satisfaction. Additionally, effort expectancy, performance expectancy, social influence and facilitating conditions can directly and significantly influence continuance intention. Among them, performance expectancy can directly influence continuance intention in the most significant way. This research provides solid evidence for the validity of the integrated model of ECM-ISC and UTAUT in the mHealth field, which can be a theoretical basis for mHealth operators' product R&D.

Determining Factors Affecting the Users' Participation of Online Health Communities: An Integrated Framework of Social Capital and Social Support.

As the national awareness of health keeps deepening, online health communities (OHCs) have achieved rapid development. Users' participation is critically important to the sustainable development of OHCs. Nevertheless, users usually lack the motive for participation. Based on the social capital theory, this research examines factors influencing users' participation in OHCs. The purpose of this research is to find out decisive factors that influence users' participation in OHCs, enrich the understanding of users' participation in OHCs, and help OHCs address the issue of sustainable development. The research model was empirically tested using 1277 responses from an online survey conducted in China. Data was analyzed using the structural equation modeling (SEM). We found informational support and emotional support to have significant direct effects over the structural capital, relational capital and cognitive capital of OHCs. Meanwhile, it is observed that relational capital and cognitive capital degree have a significant influence on knowledge acquisition and knowledge contribution of OHCs. For researchers this study provides a basis for further refinement of individual models of users' participation. For practitioners, understanding the social capital is crucial to users' knowledge acquisition and knowledge contribution that achieve high participation in OHCs.

Porous MgCo2O4 nanoflakes serve as electrode materials for hybrid supercapacitors with excellent performance.

In this work, MgCo2O4 microspheres (MgCo2O4 MSs) and MgCo2O4 nanoflakes (MgCo2O4 NFs) were prepared by one-step and two-step synthetic method, respectively, and combined with a post annealing treatment. Both MSs and NFs electrode materials possessed porous structure and large specific surface areas. The electrochemical properties were evaluated using three-electrode as well as two-electrode systems. The MgCo2O4 NFs delivered a specific capacity of 375.5C g-1 at 1 A g-1 together with a high rate performance (74.9%) at 10 A g-1, while the MgCo2O4 MSs exhibited 276.3C g-1 at the current density of 1 A g-1. A hybrid supercapacitor (HSC) device was assembled with a cathode made from MgCo2O4 and an anode made from activated carbon (AC) for evaluation of real applications, and it was able to run over a high voltage window (1.75 V). This MgCo2O4 NFs//AC HSC delivered a high energy density (Ed, 35.4 W h kg-1) at 950.6 W kg-1, and at the highest power density (Pd) of 8905.0 W kg-1, it could still hold 25.8 W h kg-1. On the other hand, the MgCo2O4 MSs//AC HSC device exhibited an Ed of 32.4 W h kg-1 at a Pd of 1048.0 W kg-1. Both HSCs exhibited good long-term cycling stability due to no capacity decay over 6000 cycles at 6 A g-1. The excellent electrochemical performance demonstrates that these MgCo2O4 electrode materials, especially the MgCo2O4 NFs, have great application potential for electrochemical energy storage. This synthesis method is simple and is possibly to be applied in synthesizing other transition metal oxides (TMOs)-based electrode materials with large surface area and outstanding electrochemical performance.

Evaluation of pediatric malignancies using total-body PET/CT with half-dose [18F]-FDG.

PURPOSE: To explore the impact of a true half dose of [18F]-FDG on image quality in pediatric oncological patients undergoing total-body PET/CT and investigate short acquisition times with half-dose injected activity. METHODS: One hundred pediatric oncological patients who underwent total-body PET/CT using the uEXPLORER scanner after receiving a true half dose of [18F]-FDG (1.85 MBq/kg) were retrospectively enrolled. The PET images were first reconstructed using complete 600-s data and then split into 300-s, 180-s, 60-s, 40-s, and 20-s duration groups (G600 to G20). The subjective analysis was performed using 5-point Likert scales. Objective quantitative metrics included the maximum standard uptake value (SUVmax), SUVmean, standard deviation (SD), signal-to-noise ratio (SNR), and SNRnorm of the background. The variabilities in lesion SUVmean, SUVmax, and tumor-to-background ratio (TBR) were also calculated. RESULTS: The overall image quality scores in the G600, G300, G180, and G60 groups were 4.9 ± 0.2, 4.9 ± 0.3, 4.4 ± 0.5, and 3.5 ± 0.5 points, respectively. All the lesions identified in the half-dose images were localized in the G60 images, while 56% of the lesions could be clearly identified in the G20 images. With reduced acquisition time, the SUVmax and SD of the backgrounds were gradually increased, while the TBR values showed no statistically significant differences among the groups (all p > 0.1). Using the half-dose images as a reference, the variability in the lesion SUVmax gradually increased from the G180 to G20 images, while the lesion SUVmean remained stable across all age groups. SNRnorm was highly negatively correlated with age. CONCLUSION: Total-body PET/CT with a half dose of [18F]-FDG (1.85 MBq/kg, estimated whole-body effective dose: 1.76-2.57 mSv) achieved good performance in pediatric patients, with sufficient image quality and good lesion conspicuity. Sufficient image quality and lesion conspicuity could be maintained at a fast scanning time of 60 s with half-dose activity.

Well entrapped platinum-iron nanoparticles on three-dimensional nitrogen-doped ordered mesoporous carbon as highly efficient and durable catalyst for oxygen reduction and zinc-air battery.

The high-performance and durable oxygen reduction reaction (ORR) catalyst on air cathode is a key component in assembly of Zn-air batteries. Herein, three-dimensional N-doped ordered mesoporous carbon (3D N-OMC) was first prepared with silica as a template via pyrolysis with assistance of dicyandiamide as a N-doping agent, combined by full adsorption of platinum (II) acetylacetonate (Pt(acac)2) and iron (II) phthalocyanine (FePc) via π-π interactions. After further pyrolysis of the resulting mixture, many PtFe nanoparticles were efficiently incorporated in 3D N-OMC (termed as PtFe@3D N-OMC for simplicity). Control experiments were certificated the important role of the pyrolysis temperature played in this synthesis. The resultant composite synergistically combines advantages of hierarchically accessible surfaces, highly open structure, and well-dispersed PtFe particles, which endow the PtFe@3D N-OMC with onset and half-wave potentials of 0.98 and 0.86 V in alkaline media, respectively, showing appealing catalytic activity for the ORR. Most significantly, the PtFe@3D N-OMC based Zn-air battery has a high power density of 80.57 mW cm-2 and long-term durability (220 h, 660 cycles). This work opens a new avenue for design of high-efficiency and durable ORR electrocatalysts in energy conversion and storage systems.

Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction.

BACKGROUND: To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. METHODS: The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 mm3. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The SUVmax, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. RESULTS: In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the SUVmax, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. CONCLUSIONS: The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8-0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT.

Multitime point pepsin testing can double the rate of the diagnosis of laryngopharyngeal reflux.

OBJECTIVE: To study the value of multitime point salivary pepsin testing (MTPSPT) for the diagnosis of laryngopharyngeal reflux (LPR). STUDY DESIGN: Prospective noncontrolled. METHODS: For patients who met the enrollment criteria, the reflux symptom index (RSI) and reflux finding score (RFS) were calculated and salivary pepsin testing was performed. The pepsin test was performed every hour from 7:00 a.m. to 6:00 p.m. by collecting fresh saliva samples. A single positive test result was needed for the diagnosis of LPR. The consistency in the diagnosis of LPR between the two methods was compared with the weighted Cohen's kappa statistic. RESULTS: A total of 204 patients were included. The kappa value between the two methods was 0.566 (p = .00). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MTPSPT were 76.43%, 85.94%, 92.24%, and 62.5%, respectively. We also compared a single pepsin measure at 7 a.m. with the screening results based on the RSI and RFS, and found a much lower kappa agreement value (0.223, p = .00). The sensitivity, specificity, PPV, NPV, and false-negative rate of pepsin testing at 7 a.m. (fasting) were 37.86%, 92.18%, 91.38%, 40.41%, and 58.57%, respectively. CONCLUSION: The use of the result of a single salivary pepsin test in the morning yields a relatively higher rate of missed diagnosis of LPR, and multitime point testing through a day increased the accuracy and sensitivity of detection of LPR twofold compared to a single morning fasting sample. LEVEL OF EVIDENCE: 3.