Single-voxel delay map from long-axial field-of-view PET scans.

Objective: We present an algorithm to estimate the delay between a tissue time-activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics. Methods: Whole-body scans of 15 patients divided equally among three tracers, namely [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times were estimated by fitting the cumulatively summed input function and tissue time-activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation: name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time-activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution. Results: Our proposed model performs better for low signal-to-noise ratio time-activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time-activity curves showed only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays. Conclusion: The algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.

Advances and challenges in immunoPET methodology.

Immuno-positron emission tomography (immunoPET) enables imaging of specific targets that play a role in targeted therapy and immunotherapy, such as antigens on cell membranes, targets in the disease microenvironment, or immune cells. The most common immunoPET applications use a monoclonal antibody labeled with a relatively long-lived positron emitter such as 89Zr (T 1/2 = 78.4 h), but smaller antibody-based constructs labeled with various other positron emitting radionuclides are also being investigated. This molecular imaging technique can thus guide the development of new drugs and may have a pivotal role in selecting patients for a particular therapy. In early phase immunoPET trials, multiple imaging time points are used to examine the time-dependent biodistribution and to determine the optimal imaging time point, which may be several days after tracer injection due to the slow kinetics of larger molecules. Once this has been established, usually only one static scan is performed and semi-quantitative values are reported. However, total PET uptake of a tracer is the sum of specific and nonspecific uptake. In addition, uptake may be affected by other factors such as perfusion, pre-/co-administration of the unlabeled molecule, and the treatment schedule. This article reviews imaging methodologies used in immunoPET studies and is divided into two parts. The first part summarizes the vast majority of clinical immunoPET studies applying semi-quantitative methodologies. The second part focuses on a handful of studies applying pharmacokinetic models and includes preclinical and simulation studies. Finally, the potential and challenges of immunoPET quantification methodologies are discussed within the context of the recent technological advancements provided by long axial field of view PET/CT scanners.

Homogeneous continuous flow nitration of O-methylisouronium sulfate and its optimization by kinetic modeling.

Nitration of O-methylisouronium sulfate under mixed acid conditions gives O-methyl-N-nitroisourea, a key intermediate of neonicotinoid insecticides with high application value. The reaction is a fast and highly exothermic process with a high mass transfer resistance, making its control difficult and risky. In this paper, a homogeneous continuous flow microreactor system was developed for the nitration of O-methylisouronium sulfate under high concentrations of mixed acids, with a homemade static mixer eliminating the mass transfer resistance. In addition, the kinetic modeling of this reaction was performed based on the theory of NO2 + attack, with the activation energy and pre-exponential factor determined. Finally, based on the response surface generated by the kinetic model, the reaction was optimized with a conversion of 87.4% under a sulfuric acid mass fraction of 94%, initial reactant concentration of 0.5 mol/L, reaction temperature of 40 °C, molar ratio of reactants at 4.4:1, and a residence time of 12.36 minutes.

Analysis of phosphofructokinase-1 activity as affected by pH and ATP concentration.

A key player in energy metabolism is phosphofructokinase-1 (PFK1) whose activity and behavior strongly influence glycolysis and thus have implications in many areas. In this research, PFK1 assays were performed to convert F6P and ATP into F-1,6-P and ADP for varied pH and ATP concentrations. PFK1 activity was assessed by evaluating F-1,6-P generation velocity in two ways: (1) directly calculating the time slope from the first two or more datapoints of measured product concentration (the initial-velocity method), and (2) by fitting all the datapoints with a differential equation explicitly representing the effects of ATP and pH (the modeling method). Similar general trends of inhibition were shown by both methods, but the former gives only a qualitative picture while the modeling method yields the degree of inhibition because the model can separate the two simultaneous roles of ATP as both a substrate of reaction and an inhibitor of PFK1. Analysis based on the model suggests that the ATP affinity is much greater to the PFK1 catalytic site than to the inhibitory site, but the inhibited ATP-PFK1-ATP complex is much slower than the uninhibited PFK1-ATP complex in product generation, leading to reduced overall reaction velocity when ATP concentration increases. The initial-velocity method is simple and useful for general observation of enzyme activity while the modeling method has advantages in quantifying the inhibition effects and providing insights into the process.

Investigation of kinetics, reaction mechanisms, thermodynamics, and synergetic effects in co-pyrolysis of wood sawdust and linear low-density polyethylene using the thermogravimetric approach.

This study focused on investigating thermal degradation behaviors, kinetics, reaction mechanisms, synergistic effects, and thermodynamic parameters of wood sawdust (WSD), linear low-density polyethylene (LLDPE), and their blends (LW1:3, LW1:1, and LW3:1) during co-pyrolysis in a thermogravimetric analyzer (TGA). Thermal behavior exhibited a LW1:3 blend (25 wt.% LLDPE) showing significant mass loss at lower temperatures (150 to 300 °C) compared to the individual feedstocks, such as 150 to 400 °C and 300 to 520 °C for WSD and LLDPE, respectively. The iso-conversional methods (KAS, FWO, and FM) were used to determine the kinetic parameters (Ea and A), and the activation energy drop was highest for the LW1:3 blend. According to the master plots, the third-order reaction (O3), nucleation (P2/3), and diffusional model (D4) were the predominant reaction mechanisms for the co-pyrolysis of the LW1:3, LW1:1, and LW3:1 blend, respectively. The thermodynamic parameters demonstrate that a small amount of plastic addition into WSD can improve the reactivity of the blend, shorten the reaction time, and cause less energy-intensive reactions. The values of ΔH, ΔG, and ΔS also confirmed the co-pyrolysis process's spontaneity and endothermic nature. The Fourier transforms infrared spectrometer (FTIR) spectra of raw feedstock, blends, and their biochar revealed some of the peaks were shifted, the intensity was reduced, and disappearance can happen when the temperature was increased. Using the experimental and theoretical/predicted activation energies, the parity chart illustrates the synergistic effects of co-pyrolysis of different blends, and the LW1:3 blend has a favorable synergistic impact. These results could be helpful in process optimization and designing an effective reactor system for co-pyrolysis.

Overlooked Complexation and Competition Effects of Phenolic Contaminants in a Mn(II)/Nitrilotriacetic Acid/Peroxymonosulfate System: Inhibited Generation of Primary and Secondary High-Valent Manganese Species.

Organic contaminants with lower Hammett constants are typically more prone to being attacked by reactive oxygen species (ROS) in advanced oxidation processes (AOPs). However, the interactions of an organic contaminant with catalytic centers and participating ROS are complex and lack an in-depth understanding. In this work, we observed an abnormal phenomenon in AOPs that the degradation of electron-rich phenolics, such as 4-methoxyphenol, acetaminophen, and 4-presol, was unexpectedly slower than electron-deficient phenolics in a Mn(II)/nitrilotriacetic acid/peroxymonosulfate (Mn(II)/NTA/PMS) system. The established quantitative structure-activity relationship revealed a volcano-type dependence of the degradation rates on the Hammett constants of pollutants. Leveraging substantial analytical techniques and modeling analysis, we concluded that the electron-rich phenolics would inhibit the generation of both primary (Mn(III)NTA) and secondary (Mn(V)NTA) high-valent manganese species through complexation and competition effects. Specifically, the electron-rich phenolics would form a hydrogen bond with Mn(II)/NTA/PMS through outer-sphere interactions, thereby reducing the electrophilic reactivity of PMS to accept the electron transfer from Mn(II)NTA, and slowing down the generation of reactive Mn(III)NTA. Furthermore, the generated Mn(III)NTA is more inclined to react with electron-rich phenolics than PMS due to their higher reaction rate constants (8314 ± 440, 6372 ± 146, and 6919 ± 31 M-1 s-1 for 4-methoxyphenol, acetaminophen, and 4-presol, respectively, as compared with 671 M-1 s-1 for PMS). Consequently, the two-stage inhibition impeded the generation of Mn(V)NTA. In contrast, the complexation and competition effects are insignificant for electron-deficient phenolics, leading to declined reaction rates when the Hammett constants of pollutants increase. For practical applications, such complexation and competition effects would cause the degradation of electron-rich phenolics to be more susceptible to water matrixes, whereas the degradation of electron-deficient phenolics remains largely unaffected. Overall, this study elucidated the intricate interaction mechanisms between contaminants and reactive metal species at both the electronic and kinetic levels, further illuminating their implications for practical treatment.

Reproducibility of [18F]MK-6240 kinetics in brain studies with shortened dynamic PET protocol in healthy/cognitively normal subjects.

BACKGROUND: [18F]MK-6240 is a neurofibrillary tangles PET radiotracer that has been broadly used in aging and Alzheimer's disease (AD) studies. Majority of [18F]MK-6240 PET studies use dynamic acquisitions longer than 60 min to assess the tracer kinetic parameters. As of today, no consensus has been established on the optimum dynamic PET scan time. In this study, we assess the reproducibility of [18F]MK-6240 quantitative metrics using shortest dynamic PET protocols in cognitively normal subjects. PET metrics were measured through two-tissue compartment model (2TCM) and Logan model to estimate VT and DVR, as well as SUVR from 90 to 120 min (SUVR90 - 120 min) post-tracer injection for brain regions. 2TCM was carried out using the 120 min dynamic coffee break dataset (first scan from 0 to 60 min p.i., second scan from 90 to 120 min p.i.) and then repeated after stepwise shortening it by 5 min. The dynamic scan length that reproduced the 120 min dynamic scans-based VT to within 10% error was defined as the shortest acquisition time (SAT). The SAT SUVR90 - 120 min was deduced from the SAT dataset by extrapolation of each image pixel time-activity curve to 120 min. The reproducibility of the 120 min dynamic scans-based VT2TCM, DVR2TCM, DVRLogan, and SUVR using the SAT was assessed using Passing-Bablock analysis. The limits of reproducibility of each PET metrics were determined using Bland-Altman analysis. RESULTS: A dynamic SAT of 40 min yielded < 10% error in [18F]MK-6240 VT2TCM's for all brain regions, compared to those measured using the 120 min datasets. SAT-based analysis did not show statistically significant systemic or proportional biases in VT2TCM, DVR2TCM, DVRLogan, or SUVR compared to those deduced from the full dynamic dataset of 120 min. A mean difference between the 120 min- and SAT-based analysis of less than 4%, 10%, 15%, and 20% existed in the VT2TCM, DVR2TCM, DVRLogan, and SUVR respectively. CONCLUSION: Kinetic modeling of [18F]MK-6240 PET can be accurately performed using dynamic scan times as short as 40 min. This can facilitate studies with [18F]MK-6240 PET and improve patients accrual. Further work would be necessary to confirm the reproducibility of these results for patients in dementia spectra.

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

Quantitative total-body PET imaging of blood flow can be performed with freely diffusible flow radiotracers such as 15O-water and 11C-butanol, but their short half-lives necessitate close access to a cyclotron. Past efforts to measure blood flow with the widely available radiotracer 18F-fluorodeoxyglucose (FDG) were limited to tissues with high 18F-FDG extraction fraction. In this study, we developed an early-dynamic 18F-FDG PET method with high temporal resolution kinetic modeling to assess total-body blood flow based on deriving the vascular transit time of 18F-FDG and conducted a pilot comparison study against a 11C-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 18F-FDG measurements of blood flow against a flow radiotracer, we analyzed total-body dynamic PET images of six human participants scanned with both 18F-FDG and 11C-butanol. An additional thirty-four total-body dynamic 18F-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: 18F-FDG blood flow was in quantitative agreement with flow measured from 11C-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/cm3 in the cerebral cortex and 2.03±0.64 ml/min/cm3 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 18F-FDG PET with high-temporal resolution kinetic modeling. Combined with standard 18F-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.

GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework.

Understanding the complex biosynthetic pathways of glycosylation is crucial for the expanding field of glycosciences. Computer-aided glycosylation analysis has greatly benefited in recent years from the development of tools found in web-based portals and open-source libraries. However, the in silico analysis of cellular glycosylation kinetics is underrepresented in current glycoscience-related tools and databases. This could be partly attributed to the limited accessibility of kinetic models developed using proprietary software and the difficulty in reliably parameterising such models. This work aims to address these challenges by proposing GlyCompute, an open-source framework demonstrating a novel, streamlined approach for the assembly, simulation, and parameterisation of kinetic models of protein N-linked glycosylation. Specifically, given one or more sets of experimentally observed N-glycan structures and their relative abundances, minimum representations of a glycosylation reaction network are generated. The topology of the resulting networks is then used to automatically assemble the material balances and kinetic mechanisms underpinning the mathematical model. To match the experimentally observed relative abundances, a sequential parameter estimation strategy using Bayesian inference is proposed, with stages determined automatically based on the underlying network topology. The proposed framework was tested on a case study involving the simultaneous fitting of the kinetic model to two protein N-linked glycoprofiles produced by the same CHO cell culture, showing good agreement with experimental observations. We envision that GlyCompute could help glycoscientists gain quantitative insights into the effect of enzyme kinetics and their perturbations on experimentally observed glycoprofiles in biomanufacturing and clinical settings.

Production of recombinant human insulin from a promising Pseudomonas fluorescens cell factory and its kinetic modeling.

Insulin intake is recommended for diabetics in addition to a proper diet and lifestyle to maintain adequate blood glucose level. Currently, there is a need for an alternative expression system for insulin production as the current expression systems may not meet the growing demand due to various constraints. Here, we demonstrate the synthesis of human insulin in an unconventional expression system based on Pseudomonas fluorescens, a BSL 1 bacterium. Human insulin was produced in the form of proinsulin fused with fusion protein. Then, the proinsulin fusion protein was purified using Ni-NTA chromatography and converted into human insulin. The physicochemical parameters for producing proinsulin fusion protein are optimized. Glucose and ammonium chloride are determined to be suitable carbon and nitrogen sources, respectively. The validity of insulin and proinsulin fusion protein is assessed using western blot and quantified using ELISA techniques. Up to 145.35 mg/l of the proinsulin fusion protein is achieved at the shake flask level. Further, MALDI-TOF and RP-HPLC analysis of the purified human insulin were observed to be close to the theoretical value and insulin standard, respectively. The expression of the recombinant fusion protein was found to be 214.7 mg/l in a batch bioreactor, a âˆ¼48% enhancement over the shake flask level. Further, kinetic modeling was performed to understand the system regarding growth, substrate utilization and product formation, and to estimate the various kinetic parameters. This study establishes the potential of the P. fluorescens expression system for producing human insulin.

Discovery and evaluation of a novel 18F-labeled vasopressin 1a receptor PET ligand with peripheral binding specificity.

The arginine-vasopressin (AVP) hormone plays a pivotal role in regulating various physiological processes, such as hormone secretion, cardiovascular modulation, and social behavior. Recent studies have highlighted the V1a receptor as a promising therapeutic target. In-depth insights into V1a receptor-related pathologies, attained through in vivo imaging and quantification in both peripheral organs and the central nervous system (CNS), could significantly advance the development of effective V1a inhibitors. To address this need, we develop a novel V1a-targeted positron emission tomography (PET) ligand, [18F]V1A-2303 ([18F]8), which demonstrates favorable in vitro binding affinity and selectivity for the V1a receptor. Specific tracer binding in peripheral tissues was also confirmed through rigorous cell uptake studies, autoradiography, biodistribution assessments. Furthermore, [18F]8 was employed in PET imaging and arterial blood sampling studies in healthy rhesus monkeys to assess its brain permeability and specificity, whole-body distribution, and kinetic properties. Our research indicated [18F]8 as a valuable tool for noninvasively studying V1a receptors in peripheral organs, and as a foundational element for the development of next-generation, brain-penetrant ligands specifically designed for the CNS.

Precise metabolic modeling in post-omics era: accomplishments and perspectives.

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

UV-induced reactive species dynamics and product formation by chlorite.

Chlorite (ClO2-) is a regulated byproduct of chlorine dioxide water treatment processes. The transformation of chlorite under UV irradiation into chloride (Cl-) and chlorate (ClO3-) involves reactive species chain reactions that could enhance chlorine dioxide water treatment efficiency while reducing residual chlorite levels. This study conducted a mechanistic investigation of chlorite phototransformation by analyzing reaction intermediates and stable end products, including chlorine dioxide (ClO2), free chlorine (HOCl/OCl-), hydroxyl­radical (•OH), Cl-, and ClO3- through combined experimental and modeling approaches. Experiments were performed at UV254 irradiation in pure buffered water within the pH range of 6 to 8. Results indicated that the apparent quantum yields for chlorite phototransformation increased from 0.86 to 1.45, and steady-state •OH concentrations at 1 mM initial chlorite concentration rose from 8.16 × 10-14 M - 16.1 × 10-14 M with decreasing pH values. It was observed that under UV irradiation, chlorite acts as both a significant producer and consumer of reactive species through three distinct reaction pathways. The developed kinetic model, which incorporates optimized intrinsic chlorite quantum yields Φchloritein ranging from 0.33 to 0.39, effectively simulated the loss of oxidants and the formation of major products. It also accurately predicted steady-state concentrations of various species, including •OH, •ClO, Cl• and O3. For the first time, this study provides a comprehensive transformation pathway scheme for chlorite phototransformation. The findings offer important insights into the mechanistic aspects of product and oxidizing species formation during chlorite phototransformation.

Noninvasive quantification of [18F]SynVesT-1 binding using simplified reference tissue model 2.

PURPOSE: [18F]SynVesT-1, a positron emission tomography (PET) radiotracer for the synaptic vesicle glycoprotein 2A (SV2A), demonstrates kinetics similar to [11C]UCB-J, with high brain uptake, fast kinetics fitting well with the one-tissue compartment (1TC) model, and excellent test-retest reproducibility. Challenges arise due to the similarity between k2 and [Formula: see text] (efflux rate of the reference region), when applying the simplified reference tissue model (SRTM) and related methods in [11C]UCB-J studies to accurately estimate [Formula: see text]. This study evaluated the suitability of these methods to estimate [18F]SynVesT-1 binding using centrum semiovale (CS) or cerebellum (CER) as reference regions. METHOD: Seven healthy participants underwent 120-min PET scans on the HRRT scanner with [18F]SynVesT-1. Six participants underwent test and retest scans. Arterial blood sampling and metabolite analysis provided input functions for the 1TC model, serving as the gold standard for kinetic parameters values. SRTM, coupled SRTM (SRTMC) and SRTM2 estimated were applied to estimate [Formula: see text](ref: CS) and DVRCER(ref: CER) values. For SRTM2, the population average of [Formula: see text] was determined from the 1TC model applied to the reference region. Test-retest variability and minimum scan time were also calculated. RESULTS: The 1TC k2 (1/min) values for CS and CER were 0.031 ± 0.004 and 0.021 ± 0.002, respectively. Although SRTMC [Formula: see text] was much higher than 1TC [Formula: see text], SRTMC underestimated BPND(ref: CS) and DVRCER by an average of 3% and 1% across regions, respectively, due to similar bias in k2 and [Formula: see text] estimation. SRTM underestimated BPND(ref: CS) by an average of 3%, but with the CER as reference region, SRTM estimation was unstable and DVRCER underestimation varied by region (mean 10%). Using population average [Formula: see text] values, SRTM2 BPND and DVRCER showed the best agreement with 1TC estimates. CONCLUSION: Our findings support the use of population [Formula: see text] value in SRTM2 with [18F]SynVesT-1 for the estimation of [Formula: see text] or DVRCER, regardless of the choice of reference region.

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.

Formation and kinetic analysis of AGEs in Pacific white shrimp during frying.

Advanced glycation end products (AGEs) are complex and heterogeneous compounds closely associated with various chronic diseases. The changes in Nε-carboxymethyllysine (CML), Nε-carboxyethyllysine (CEL), Nε-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1), and fluorescent AGEs (F-AGEs) in fried shrimp during frying (170 °C, 0-210 s) were described by kinetic models. Besides,the correlations between AGEs contents and physicochemical indicators were analyzed to reveal their intrinsic relationship. Results showed that the changes of four AGEs contents followed the zero-order kinetic, and their rate constants were ranked as kCML < kCEL ≈ kMG-H1 < kF-AGEs. Oil content and lipid oxidation were critical factors that affected the AGEs levels of the surface layer. Protein content and Maillard reaction were major factors in enhancing the CML and CEL levels of the interior layer. Furthermore, the impact of temperature on the generation of CML and CEL was greater than that of MG-H1 and F-AGEs.

Mössbauer and EPR detection of iron trafficking kinetics and possibly labile iron pools in whole Saccharomyces cerevisiae cells.

The kinetics of iron trafficking in whole respiring Saccharomyces cerevisiae cells were investigated using Mössbauer and EPR spectroscopies. The Mössbauer-active isotope 57Fe was added to cells growing under iron-limited conditions; cells were analyzed at different times post iron addition. Spectroscopic changes suggested that the added 57Fe initially entered the labile iron pool, and then distributed to vacuoles and mitochondria. The first spectroscopic feature observed, ∼ 3 min after adding 57Fe plus a 5 to 15 min processing dead time, was a quadrupole doublet typical of nonheme high-spin FeII. This feature likely arose from labile FeII pools in the cell. At later times (15-150 min), magnetic features due to S = 5/2 FeIII developed; these likely arose from FeIII in vacuoles. Corresponding EPR spectra were dominated by a g = 4.3 signal from the S = 5/2 FeIII ions that increased in intensity over time. Developing at a similar rate was a quadrupole doublet typical of S = 0 [Fe4S4]2+ clusters and low-spin FeII hemes; such centers are mainly in mitochondria, cytosol, and nuclei. Development of these features was simulated using a published mathematical model, and simulations compared qualitatively well with observations. In the five sets of experiments presented, all spectroscopic features developed within the doubling time of the cells, implying that the detected iron trafficking species are physiologically relevant. These spectroscopy-based experiments allow the endogenous labile iron pool within growing cells to be detected without damaging or altering the pool, as definitely occurs using chelator-probe detection and possibly occurs using chromatographic separations.

A highly efficient modified nano-activated carbon adsorbent for separation of ammonia from water: Experimental and kinetics elucidations.

Water is essential for the survival of all living things; however, its extensive use in agriculture, high-tech manufacturing, energy production, and the rapid development of the chemical and petroleum industries has led to significant contamination, making water pollution a major concern today. Ammonia is one of the most harmful contaminants present in water, posing significant environmental and health risks that require appropriate remediation methods. To remove ammonia from contaminated water, we employ Carbon Nanotubes (CNTs) and Activated Carbon (AC). To ensure appropriate metal impregnation on the adsorbents, Fe, Al, Ag, and Cu were impregnated into both CNT and AC, followed by extensive characterization using Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), and Energy Dispersive X-rays (EDX). To optimize ammonia removal from water, several parameters were adjusted, including pH, dose amount, contact time, shaking speed, and temperature. Astonishingly, the highest removal efficiency of 40% was achieved with a 1 g dosage at pH 10.5 and 200 RPM, while silver oxide had a lower removal rate of 10% under the same conditions. Temperature additionally had a significant impact, with removal percentages reaching 40% at 70 °C as compared to 21.5% at 25 °C. Adsorption isotherms were used to analyze the experimental data, along with Langmuir and Freundlich's models. Notably, Langmuir produced superior curve fitting, resulting in a correlation factor close to one. Furthermore, kinetic modeling was carried out with 2nd-order and pseudo-2nd-order equations, with the latter responding better according to curve analysis. Because the ammonia removal rate was low, this study indicates the feasibility of implementing an adsorption technique using CNT and AC as a pre-treatment method for this purpose. This approach has the potential for future optimization and deployment in tackling water contamination concerns effectively.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

Photo-reductive decomposition of perfluorooctane sulfonate (PFOS) and its common alternatives by UV/VUV/sulfite process: Mechanism, kinetic modeling, and water matrix effects.

The present study investigated the photo-reduction of perfluorooctane sulfonate (PFOS) and its alternatives, focusing on decomposition mechanisms, active species involvement, the influence of background water constituents, and kinetic model development. The decomposition and defluorination rates followed the order of PFOS > PFHxS > 6:2 FTSA > PFBS, with shorter chains and CH2 linkers enhancing the resistance of PFOS alternatives against the attack of hydrated electrons (eaq-). Two primary pathways were identified during the photodegradation of PFAS: (i) H/F exchange at CF bonds with the lowest bond dissociation energies (BDEs) and (ii) functional group cleavage followed by short-chain PFCAs formation, with OH playing a crucial role in transforming intermediates. Adding iodide and elevated temperatures demonstrated a synergistic effect on PFBS decomposition and defluorination, with high temperatures promoting functional group cleavage as the preferred defluorination pathway. The study examined the impact of background water constituents in different aqueous environments, from surface waters to wastewater streams and ion-exchange brine concentrates. Chloride exhibited a concentration-based dual impact on the UV/VUV/sulfite process: promotive effects at low dosages (1-10 mM) by acting as a secondary eaq- mediator, and adverse effects at high dosages (20-500 mM) due to the scavenging effect of generated chlorine radicals (Cl). High ionic strength adversely affected eaq- quantum efficiency. Additionally, bicarbonate and natural organic matter (NOM) had opposing effects on PFOS photo-reduction, primarily through eaq- scavenging and pH alteration. Kinetic modeling revealed reaction rate constants of the studied PFAS with eaq- ranging from 1.8 × 106 to 1.3 × 109 M-1 s-1, corroborating the concentration profiles of active species and highlighting the reductive nature of sulfite-mediated processes.