Cluster Analysis to Identify Long COVID Phenotypes Using 129Xe Magnetic Resonance Imaging: A Multi-centre Evaluation.

BACKGROUND: Long COVID impacts ∼10% of people diagnosed with COVID-19, yet the pathophysiology driving ongoing symptoms is poorly understood. We hypothesised that 129Xe magnetic resonance imaging (MRI) could identify unique pulmonary phenotypic subgroups of long COVID, therefore we evaluated ventilation and gas exchange measurements with cluster analysis to generate imaging-based phenotypes. METHODS: COVID-negative controls and participants who previously tested positive for COVID-19 underwent 129XeMRI ∼14-months post-acute infection across three centres. Long COVID was defined as persistent dyspnea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully-recovered. 129XeMRI ventilation defect percent (VDP) and membrane (Mem)/Gas, red blood cell (RBC)/Mem and RBC/Gas ratios were used in k-means clustering for long COVID, and measurements were compared using ANOVA with post-hoc Bonferroni correction. RESULTS: We evaluated 135 participants across three centres: 28 COVID-negative (40±16yrs), 34 fully-recovered (42±14yrs) and 73 long COVID (49±13yrs). RBC/Mem (p=0.03) and FEV1 (p=0.04) were different between long- and COVID-negative; FEV1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster1 was the youngest with normal MRI and mild gas-trapping; Cluster2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster3 had mildly increased Mem/Gas with normal PFTs; and Cluster4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern. CONCLUSION: We identified four 129XeMRI long COVID phenotypes with distinct characteristics. 129XeMRI can dissect pathophysiologic heterogeneity of long COVID to enable personalised patient care.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE: Retrospective. SUBJECTS: 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). DATA CONCLUSION: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. TECHNICAL EFFICACY: Stage 2.

Trilaciclib dosage in Chinese patients with extensive-stage small cell lung cancer: a pooled pharmacometrics analysis.

This study aimed to analyze potential ethnic disparities in the dose-exposure-response relationships of trilaciclib, a first-in-class intravenous cyclin-dependent kinase 4/6 inhibitor for treating chemotherapy-induced myelosuppression in patients with extensive-stage small cell lung cancer (ES-SCLC). This investigation focused on characterizing these relationships in both Chinese and non-Chinese patients to further refine the dosing regimen for trilaciclib in Chinese patients with ES-SCLC. Population pharmacokinetic (PopPK) and exposure-response (E-R) analyses were conducted using pooled data from four randomized phase 2/3 trials involving Chinese and non-Chinese patients with ES-SCLC. PopPK analysis revealed that trilaciclib clearance in Chinese patients was approximately 17% higher than that in non-Chinese patients with ES-SCLC. Sex and body surface area influenced trilaciclib pharmacokinetics in both populations but did not exert a significant clinical impact. E-R analysis demonstrated that trilaciclib exposure increased with a dosage escalation from 200 to 280 mg/m2, without notable changes in myeloprotective or antitumor efficacy. However, the incidence of infusion site reactions, headaches, and phlebitis/thrombophlebitis rose with increasing trilaciclib exposure in both Chinese and non-Chinese patients with ES-SCLC. These findings suggest no substantial ethnic disparities in the dose-exposure-response relationship between Chinese and non-Chinese patients. They support the adoption of a 240-mg/m2 intravenous 3-day or 5-day dosing regimen for trilaciclib in Chinese patients with ES-SCLC.

Efficacy and safety of Mahuang Fuzi and Shenzhuo Decoction for treatment of primary membranous nephropathy: a multicenter prospective trial.

BACKGROUND: This study aims to undertake a comprehensive assessment of the effectiveness and safety profile of Mahuang Fuzi and Shenzhuo Decoction (MFSD) in the management of primary membranous nephropathy (PMN), within the context of a prospective clinical investigation. METHODS: A multicenter, open-label clinical trial was executed on patients diagnosed with PMN. These individuals were subjected to MFSD therapy for a duration of at least 24 months, with primary outcome of clinical remission rates. The Cox regression analysis was employed to discern the pertinent risk factors exerting influence on the efficacy of MFSD treatment, with scrupulous monitoring of any adverse events. RESULTS: The study comprised 198 participants in total. Following 24 months of treatment, the remission rate was 58.6% (116/198). Among the subgroup of 130 participants subjected to a 36-month follow-up, the remission rate reached 70% (91/130). Subgroup analysis revealed that neither a history of immunosuppressive therapy (HIST) nor an age threshold of ≥60 years exhibited a statistically significant impact on the remission rate at the 24-month mark (p > .05). Multivariate Cox regression analyses elucidated HIST, nephrotic syndrome, or mass proteinuria, and a high-risk classification as noteworthy risk factors in the context of MFSD treatment. Remarkably, no fatalities resulting from side effects were documented throughout the study's duration. CONCLUSIONS: This trial establishes the efficacy of MFSD as a treatment modality for membranous nephropathy. MFSD demonstrates a favorable side effect profile, and remission rates are consistent across patients, irrespective of HIST and age categories.

Population pharmacokinetic modeling of caffeine in preterm infants with apnea of prematurity: New findings from concomitant erythromycin and AHR genetic polymorphisms.

Current standard-dose caffeine therapy results in significant intersubject variability. The aims of this study were to develop and evaluate population pharmacokinetic (PPK) models of caffeine in preterm infants through comprehensive screening of covariates and then to propose model-informed precision dosing of caffeine for this population. A total of 129 caffeine concentrations from 96 premature neonates were incorporated into this study. Comprehensive medical record and genotype data of these neonates were collected for analysis. PPK modeling was performed by a nonlinear mixed effects modeling program (NONMEM). Final models based on the current weight (CW) or body surface area (BSA) were evaluated via multiple graphic and statistical methods. The model-informed dosing regimen was performed through Monte Carlo simulations. In addition to CW or BSA, postnatal age, coadministration with erythromycin (ERY), and aryl hydrocarbon receptor coding gene (AHR) variant (rs2158041) were incorporated into the final PPK models. Multiple evaluation results showed satisfactory prediction performance and stability of the CW- and BSA-based models. Monte Carlo simulations demonstrated that trough concentrations of caffeine in preterm infants would be affected by concomitant ERY therapy and rs2158041 under varying dose regimens. For the first time, ERY and rs2158041 were found to be associated with the clearance of caffeine in premature infants. Similar predictive performance and stability were obtained for both CW- and BSA-based PPK models. These findings provide novel insights into caffeine precision therapy for preterm infants.

Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations.

BACKGROUND: With the spread of COVID-19, the time-series prediction of COVID-19 has become a research hotspot. Unlike previous epidemics, COVID-19 has a new pattern of long-time series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with short-time series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms. METHODS: A dynamical model with only infected humans and removed humans was established. Then the process of COVID-19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a non-linear variable and NO2 concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions. RESULTS: The experiments and analysis showed the R2 of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. CONCLUSION: The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID-19 with local fluctuations and multiple peaks, which can provide valuable assistant decision-making information.

Therapeutic drug monitoring of caffeine and its primary metabolites in plasma using LC-ESI-MS/MS for apnea of prematurity treatment: Evaluation of ultrapure water as a surrogate matrix.

The growing evidence has endorsed the view that therapeutic drug monitoring of caffeine for apnea of prematurity is helpful for dose tailoring when the therapeutic response is lacking or toxicity is suspected. However, plasma without caffeine is difficult to obtain. Therefore, a method was developed and validated to measure caffeine and its three primary metabolites (paraxanthine, theobromine and theophylline) using LC-ESI-MS/MS in human plasma and several surrogate matrices. The chromatographic separation of analytes was finally achieved on a Waters Symmetry C18 (4.6 × 75 mm, 3.5 µm) column. Several strategies were successfully applied to overcome the matrix effects: (a) appropriate dilution for sample cleanup; (b) a starting lower proportion of organic phase; and (c) multiple individual stable-labeled isotopic internal standards. The parallelism between the authentic matrix and surrogate matrices was convincing. The recovery of the analytes in both human plasma and rat plasma was acceptable over the linear range (0.500-50.0 µg/ml for caffeine and 0.0100-1.00 µg/ml for three metabolites). The method was successfully applied in 118 samples from 74 preterm infants with apnea of prematurity. The rat plasma or ultrapure water as a surrogate matrix is worthy of recommendation for routine therapeutic drug monitoring of caffeine.

Calibration and Testing of Small High-Resolution Transition Edge Sensor Microcalorimeters with Optical Photons.

Pulses of narrow line-width optical photons can be used to calibrate and test sub-2 eV full-width at halfmaximum (FWHM) energy resolution transition-edge sensor (TES) microcalorimeters at low energies (< 1 keV), where it is very challenging to obtain X-ray calibration lines comparable to (or narrower than) the detector resolution. This scheme depends on the ability to resolve the number of 3 eV photons in each pulse, which we have recently demonstrated up to photon numbers of about 300. At LTD-18 we showed preliminary results obtained with this technique on a 0.25 eV baseline resolution TES microcalorimeter designed for the ultra-high-resolution subarray of the Lynx mission. The line-shape was well described by a simple Gaussian. However, the difficulty of delivering photons to the small 46 µm square absorbers resulted in a large thermal crosstalk signal, whose random nature is expected to rapidly degrade the observed energy resolution towards higher photon numbers/energies. We have since improved the coupling between the optical fiber and the TES absorber and report here our current results.

Experimental models for elderly patients with membranous nephropathy: Application and advancements.

Membranous nephropathy (MN) occurs predominantly in middle-aged and elderly individuals and ranks among the most prevalent etiologies of elderly nephrotic syndrome. As an autoimmune glomerular disorder characterized by glomerular basement membrane thickening and immune complex deposition, conventional MN animal models, including the Heymann nephritis rat model and the c-BSA mouse model, have laid a foundation for MN pathogenesis research. However, differences in target antigens between rodents and humans have impeded this work. In recent years, researchers have created antigen-specific MN animal models, primarily centered on PLA2R1 and THSD7A, employing diverse techniques that provide innovative in vivo research platforms for MN. Furthermore, significant advancements have been made in the development of in vitro podocyte models relevant to MN. This review compiles recent antigen-specific MN animal models and podocyte models, elucidates their immune responses and pathological characteristics, and offers insights into the future of MN experimental model development. Our aim is to provide a comprehensive resource for research into the pathogenesis of MN and the development of targeted therapies for older patients with MN to prolong lifespan and improve quality of life.

Development and Evaluation of a Quantitative Systems Pharmacology Model for Mechanism Interpretation and Efficacy Prediction of Atezolizumab in Combination with Carboplatin and Nab-Paclitaxel in Patients with Non-Small-Cell Lung Cancer.

Immunotherapy has shown clinical benefit in patients with non-small-cell lung cancer (NSCLC). Due to the limited response of monotherapy, combining immune checkpoint inhibitors (ICIs) and chemotherapy is considered a treatment option for advanced NSCLC. However, the mechanism of combined therapy and the potential patient population that could benefit from combined therapy remain undetermined. Here, we developed an NSCLC model based on the published quantitative systems pharmacology (QSP)-immuno-oncology platform by making necessary adjustments. After calibration and validation, the established QSP model could adequately characterise the biological mechanisms of action of the triple combination of atezolizumab, nab-paclitaxel, and carboplatin in patients with NSCLC, and identify predictive biomarkers for precision dosing. The established model could efficiently characterise the objective response rate and duration of response of the IMpower131 trial, reproducing the efficacy of alternative dosing. Furthermore, CD8+ and CD4+ T cell densities in tumours were found to be significantly related to the response status. This significant extension of the QSP model not only broadens its applicability but also more accurately reflects real-world clinical settings. Importantly, it positions the model as a critical foundation for model-informed drug development and the customisation of treatment plans, especially in the context of combining single-agent ICIs with platinum-doublet chemotherapy.

Population pharmacokinetics of valproic acid in children with epilepsy: Implications for dose tailoring when switching from oral syrup to sustained-release tablets.

Significant pharmacokinetic (PK) differences exist between different forms of valproic acid (VPA), such as syrup and sustained-release (SR) tablets. This study aimed to develop a population pharmacokinetic (PopPK) model for VPA in children with epilepsy and offer dose adjustment recommendation for switching dosage forms as needed. The study collected 1411 VPA steady-state trough concentrations (Ctrough) from 617 children with epilepsy. Using NONMEM software, a PopPK model was developed, employing a stepwise approach to identify possible variables such as demographic information and concomitant medications. The final model underwent internal and external evaluation via graphical and statistical methods. Moreover, Monte Carlo simulations were used to generate a dose tailoring strategy for typical patients weighting 20-50 kg. As a result, the PK characteristics of VPA were described using a one-compartment model with first-order absorption. The absorption rate constant (ka) was set at 2.64 and 0.46 h-1 for syrup and SR tablets. Body weight and sex were identified as significant factors affecting VPA's pharmacokinetics. The final PopPK model demonstrated acceptable prediction performance and stability during internal and external evaluation. For children taking syrup, a daily dose of 25 mg/kg resulted in the highest probability of achieving the desired target Ctrough, while a dose of 20 mg/kg/day was appropriate for those taking SR tablets. In conclusion, we established a PopPK model for VPA in children with epilepsy to tailor VPA dosage when switching between syrup and SR tablets, aiming to improve plasma VPA concentrations fluctuations.

Activation of the IL-6/STAT3 pathway contributes to the pathogenesis of membranous nephropathy and is a target for Mahuang Fuzi and Shenzhuo Decoction (MFSD) to repair podocyte damage.

BACKGROUND: Primary membranous nephropathy (PMN) is an autoimmune glomerular disease. IL-6 is a potential therapeutic target for PMN. Previous clinical studies have demonstrated the effectiveness of Mahuang Fuzi and Shenzhuo Decoction (MFSD) in treating membranous nephropathy. However, the mechanism of action of MFSD remains unclear. METHODS: Serum IL-6 levels were measured in patients with PMN and healthy subjects. The passive Heymann nephritis (PHN) rat model was established, and high and low doses of MFSD were used for intervention to observe the repair effect of MFSD on renal pathological changes and podocyte injury. RNA-seq was used to screen the possible targets of MFSD, and the effect of MFSD targeting IL-6/STAT3 was further verified by combining the experimental results. Finally, the efficacy of tocilizumab in PHN rats was observed. RESULTS: Serum IL-6 levels were significantly higher in PMN patients than in healthy subjects. These levels significantly decreased in patients in remission after MFSD treatment. MFSD treatment improved laboratory indicators in PHN rats, as well as glomerular filtration barrier damage and podocyte marker protein expression. Renal transcriptome changes showed that MFSD-targeted differential genes were enriched in JAK/STAT and cytokine-related pathways. MFSD inhibits the IL6/STAT3 pathway in podocytes. Additionally, MFSD significantly reduced serum levels of IL-6 and other cytokines in PHN rats. However, treatment of PHN with tocilizumab did not achieve the expected effect. CONCLUSION: The IL-6/STAT3 signaling pathway is activated in podocytes of experimental membranous nephropathy. MFSD alleviates podocyte damage by inhibiting the IL-6/STAT3 pathway.

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data.

Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.

Bibliometric analysis of global endometriosis research, 2002 to 2021: A review.

Endometriosis is a common disease of reproductive-age women and an important cause of dysmenorrhea and infertility. Information on endometriosis is complex and there is a lack of summarization of available results. The study aims to evaluate the overall distribution of publications related to endometriosis to provide a foundation for further research. The Web of Science Core Collection was searched for articles published in the field of endometriosis. Our survey revealed the structure, hotspots, and development trends of endometriosis-related research and publications.

Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19.

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.

Successful LC-MS/MS assay development and validation for determination of valproic acid and its metabolites supporting proactive pharmacovigilance.

Valproic acid (VPA) is a well-documented contributor to liver injury, which is likely caused by the formation of its toxic metabolites. Monitoring VPA and its metabolites is very meaningful for the pharmacovigilance, but the availability of a powerful assay is a prerequisite. In this study, for the first time, a sensitive and specific LC-MS/MS method was developed and validated to simultaneously quantify the concentrations of VPA and its six pestering isomer metabolites (3-OH-VPA, 4-OH-VPA, 5-OH-VPA, 2-PGA, VPA-G, and 2-ene-VPA) in human plasma, using 5-OH-VPA-d7 and VPA-d6 as the internal standards (ISs). We also figured out another tricky problem that the concentrations of the parent drug and the metabolites vary widely. Of note, after protein precipitation and dilution with acetonitrile (ACN) and 50% ACN successively, the analytes and the ISs were successfully separated on a Kinetex C18 column. Intriguingly, sacrificing its signal intensity by elevated collision energy of VPA finally achieved the simultaneous determination. As expected, the method showed great linearity (r > 0.998) over the concentration ranges for all analytes. The inter-day and intra-day accuracy and precision were both acceptable. The method was successfully applied in 127 children with epilepsy. This novel assay will support the VPA-associated pharmacovigilance in the future.

Integrating metabolomics and lipidomics revealed a decrease in plasma fatty acids but an increase in triglycerides in children with drug-refractory epilepsy.

OBJECTIVE: The drug-refractory epilepsy (DRE) in children is commonly observed but the underlying mechanisms remain elusive. We examined whether fatty acids (FAs) and lipids are potentially associated with the pharmacoresistance to valproic acid (VPA) therapy. METHODS: This single-center, retrospective cohort study was conducted using data from pediatric patients collected between May 2019 and December 2019 at the Children's Hospital of Nanjing Medical University. Ninety plasma samples from 53 responders with VPA monotherapy (RE group) and 37 non-responders with VPA polytherapy (NR group) were collected. Non-targeted metabolomics and lipidomics analysis for those plasma samples were performed to compare the potential differences of small metabolites and lipids between the two groups. Plasma metabolites and lipids passing the threshold of variable importance in projection value >1, fold change >1.2 or <0.8, and p-value <0.05 were regarded as statistically different substances. RESULTS: A total of 204 small metabolites and 433 lipids comprising 16 different lipid subclasses were identified. The well-established partial least squares-discriminant analysis (PLS-DA) revealed a good separation of the RE from the NR group. The FAs and glycerophospholipids status were significantly decreased in the NR group, but their triglycerides (TG) levels were significantly increased. The trend of TG levels in routine laboratory tests was in line with the lipidomics analysis. Meanwhile, cases from the NR group were characterized by a decreased level of citric acid and L-thyroxine, but with an increased level of glucose and 2-oxoglutarate. The top two enriched metabolic pathways involved in the DRE condition were biosynthesis of unsaturated FAs and linoleic acid metabolism. SIGNIFICANCE: The results of this study suggested an association between metabolism of FAs and the medically intractable epilepsy. Such novel findings might propose a potential mechanism linked to the energy metabolism. Ketogenic acid and FAs supplementation might therefore be high-priority strategies for DRE management.

Corrigendum: MicroRNAs: Potential mediators between particulate matter 2.5 and Th17/Treg immune disorder in primary membranous nephropathy.

[This corrects the article DOI: 10.3389/fphar.2022.968256.].

Effective defluoridation of water using nanosized UiO-66-NH2 encapsulated within macroreticular polystyrene anion exchanger.

Environmental concerns associated with the efficient defluoridation of contaminated water remain a substantial challenge. In this work, a new nanocomposite, UiO-66-NH2@PS+, was successfully fabricated via in situ precipitation of a water-stable metal-organic framework (UiO-66-NH2) inside a commercial polystyrene anion exchanger PS+. The as-formed nanocomposite UiO-66-NH2@PS+ was characterized using various morphological methods, which demonstrated that nanosized UiO-66-NH2 was homogenously dispersed within the inner pores of PS+. Batch adsorption experiments indicated that UiO-66-NH2@PS+ exhibited outstanding adsorption performance for fluoride over a broad pH range of 3.0-8.0. The saturated adsorption capacity of fluoride at 298 K was 27.5 and 32.8 mg/g for pH 6.5 and 4.5 with the adsorbent dosage of 0.5 g/L and initial concentration of 5-80 mg/L. Moreover, the utilization rate of active adsorption sites of UiO-66-NH2 was greatly improved after encapsulation. The XPS study indicated that the integrated effects of specific inner-sphere coordination and ligand exchange between fluoride and UiO-66-NH2 might be the dominant adsorption mechanism. Fixed-bed tests indicated that the UiO-66-NH2@PS+ column could successively produce clean water with bed volumes of 350 and 70 ([F-] <1.5 mg/L) from simulated fluoride-pollution water at pH 4.5 and 8.0, with a liquid velocity of 20 mL/h, and an empty bed contact time (EBCT) of 15 min, which was higher than that of the other materials. In addition, the exhausted UiO-66-NH2@PS+ was regenerated and reused for 5 times through complete regeneration, highlighting the potential feasibility of defluorination in practical applications.

Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.

An efficient and safe vaccine is expected to allow people to return to normal life as soon as possible. However, vaccines for new diseases are likely to be in short supply during the initial deployment due to narrow production capacity and logistics. There is an urgent need to optimize the allocation of limited vaccines to improve the population effectiveness of vaccination. Existing studies mostly address a single epidemiological landscape. The robustness of the effectiveness of other proposed strategies is difficult to guarantee under other landscapes. In this study, a novel vaccination allocation model based on spatio-temporal heterogeneity of epidemiological landscapes is proposed. This model was combined with optimization algorithms to determine the near-optimal spatio-temporal allocation for vaccines with different effectiveness and coverage. We fully simulated the epidemiological landscapes during vaccination, and then minimized objective functions independently under various epidemiological landscapes and degrees of viral transmission. We find that if all subregions are in the middle or late stages of the pandemic, the difference between the effectiveness of the near-optimal and pro-rata strategies is very small in most cases. In contrast, under other epidemiological landscapes, when minimizing deaths, the optimizer tends to allocate the remaining doses to sub-regions with relatively higher risk and expected coverage after covering the elderly. While to minimize symptomatic infections, allocating vaccines first to the higher-risk sub-regions is near-optimal. This means that the pro-rata allocation is a good option when the subregions are all in the middle to late stages of the pandemic. Moreover, we suggest that if all subregions are in the period of rapid virus transmission, vaccines should be administered to older adults in all subregions simultaneously, while when the epidemiological dynamics of the subregions are significantly different, priority can be given to older adults in subregions that are still in the early stages of the pandemic. After covering the elderly in the region, high-risk sub-regions can be prioritized.