Assessment of OCT-Based Macular Curvature and Its Relationship with Macular Microvasculature in Children with Anisomyopia.

INTRODUCTION: To evaluate the intraocular differences in optical coherence tomography (OCT)-based macular curvature index (MCI) among children with anisomyopia and to investigate the relationship between MCI and the macular microvasculature. METHODS: Fifty-two schoolchildren with anisometropia > 2.00 D were enrolled and underwent comprehensive examinations including cycloplegic refraction, axial length (AL), and swept source OCT/OCT angiography. OCT-based MCIs were determined from horizontal and vertical B-scans by a customized curve fitting model in MATLAB R2022 at 1-mm-, 3-mm-, and 6-mm-diameter circles at fovea. Characteristics and topographic variation of MCI was analyzed, and the relationships with microvascularity and its associated factors were investigated. RESULTS: MCI achieved high reliability and repeatability. There were overall larger MCIs in the more myopic eyes than the less myopic eyes in 1-mm-, 3-mm-, and 6-mm-diameter circles at fovea (all p < 0.001). For the topographic variation, horizontal MCI was significantly greater than vertical MCI (all p < 0.001), and was the largest in 6-mm circle, followed by 3-mm and 1-mm circles. Stronger correlation of horizontal MCI with myopic severity than vertical MCI was found. Partial Pearson's correlation found MCI was negatively associated with deep capillary plexus (DCP) vessel density (p = 0.016). Eyes with a higher MCI in a 6-mm circle were more likely to have longer AL (p < 0.001), lower DCP vessel density (p = 0.037), and thinner choroidal thickness (ChT) (p = 0.045). CONCLUSION: Larger MCI was found in the more myopic eyes of children with anisomyopia and was significantly associated with smaller DCP density, suggesting that MCI was an important indicator of myopia-related retinal microvascularity change, and it could be a valuable metric for myopia assessment in children.

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.

Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes. Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People's Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution. Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model. Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.

Population pharmacokinetics and dose optimization of levofloxacin in elderly patients with pneumonia.

AIMS: Levofloxacin is a quinolone antibiotic with a broad antibacterial spectrum. It is frequently used in elderly patients with pneumonia. The pharmacokinetic profile of elderly patients changes with age, but data on the pharmacokinetics of levofloxacin in these patients are limited. The aim of this study was to establish a population pharmacokinetic model of levofloxacin in elderly patients with pneumonia and to optimize individualized dosing regimens based on this newly developed model. METHODS: This is a prospective, open-label pharmacokinetic study in elderly patients with pneumonia. Blood samples were collected using an opportunistic approach. The plasma concentrations of levofloxacin were determined by high-performance liquid chromatography. A population pharmacokinetic model was established using nonlinear mixed-effect model software. Monte Carlo simulations were used for dose simulation and dose optimization. RESULTS: Data from 51 elderly patients with pneumonia were used for the population pharmacokinetic analysis. A one-compartment model with first-order elimination was most suitable for describing the data, and the estimated glomerular filtration rate was the only covariate that had a significant impact on the model. The final model estimated that the mean clearance of levofloxacin in elderly patients with pneumonia was 5.26 L/h. Monte Carlo simulation results showed that the optimal dosing regimen for levofloxacin was 750 mg once a day in elderly patients with pneumonia, with a minimum inhibitory concentration of 2 mg/L. CONCLUSIONS: The population pharmacokinetic model of levofloxacin in elderly patients with pneumonia was established, and the dose optimization of levofloxacin was completed through Monte Carlo simulation.

Genome-wide assessment of genetic diversity and transcript variations in 17 accessions of the model diatom Phaeodactylum tricornutum.

Diatoms, a prominent group of phytoplankton, have a significant impact on both the oceanic food chain and carbon sequestration, thereby playing a crucial role in regulating the climate. These highly diverse organisms show a wide geographic distribution across various latitudes. In addition to their ecological significance, diatoms represent a vital source of bioactive compounds that are widely used in biotechnology applications. In the present study, we investigated the genetic and transcriptomic diversity of 17 accessions of the model diatom Phaeodactylum tricornutum including those sampled a century ago as well as more recently collected accessions. The analysis of the data reveals a higher genetic diversity and the emergence of novel clades, indicating an increasing diversity within the P. tricornutum population structure, compared to the previous study and a persistent long-term balancing selection of genes in old and newly sampled accessions. However, the study did not establish a clear link between the year of sampling and genetic diversity, thereby, rejecting the hypothesis of loss of heterozygoty in cultured strains. Transcript analysis identified novel transcript including noncoding RNA and other categories of small RNA such as PiwiRNAs. Additionally, transcripts analysis using differential expression as well as Weighted Gene Correlation Network Analysis has provided evidence that the suppression or downregulation of genes cannot be solely attributed to loss-of-function mutations. This implies that other contributing factors, such as epigenetic modifications, may play a crucial role in regulating gene expression. Our study provides novel genetic resources, which are now accessible through the platform PhaeoEpiview (https://PhaeoEpiView.univ-nantes.fr), that offer both ease of use and advanced tools to further investigate microalgae biology and ecology, consequently enriching our current understanding of these organisms.

Exploration of the mechanism of Qi-Xian decoction in asthmatic mice using metabolomics combined with network pharmacology.

Introduction: Qi-Xian Decoction (QXD), a traditional Chinese medicine (TCM) formula consisting of eight herbs, has been clinically used to treat asthma. However, the underlying mechanisms have not been completely elucidated. This study aimed to combine metabolomics and network pharmacology to reveal the mechanism of action of QXD in asthma treatment. Methods: An ovalbumin (OVA)-induced asthma mouse model was constructed to evaluate the therapeutic effects of QXD. Serum metabolomics and network pharmacology were combined to study the mechanism of anti-asthma action as well as the potential target, and related biological functions were validated. Results: The QXD treatment has demonstrated significant protective effects in OVA-induced asthmatic mice, as evidenced by its ability to inhibit inflammation, IgE, mucus overproduction, and airway hyperreactivity (AHR). Metabolomic analysis has revealed a total of 140 differential metabolites associated with QXD treatment. In addition, network pharmacology has identified 126 genes that are linked to the effects of QXD, including TNF, IL-6, IL1ß, STAT3, MMP9, EGFR, JUN, CCL2, TLR4, MAPK3 and MAPK8. Through comprehensive gene-metabolite interaction network analysis, seven key metabolites have been identified and associated with the potential anti-asthmatic effect of QXD, with palmitic acid (PA) being the most notable among them. In vitro validation studies have confirmed the gene-metabolite interaction involving PA, IL-6, and MAPK8. Furthermore, our research has demonstrated that QXD treatment can effectively inhibit PA-promoted IL-6 expression in MH-S cells and reduce PA concentration in OVA-induced asthmatic mice. Conclusion: The regulation of metabolic pathways by QXD was found to be associated with its anti-asthmatic action, which provides insight into the mechanism of QXD in treating asthma.

The relationship between intergenerational financial support and depressive symptoms among older adults: Evidence from China Health and Retirement Longitudinal Study, 2011-2018.

OBJECTIVE: Aimed to investigate the effect of intergenerational financial support on depressive symptoms among older adults over time. METHODS: Data were obtained from China Health and Retirement Longitudinal Study (CHARLS) 2011, 2013, 2015, and 2018. A finite distributed lag (FDL) model was employed, long-run cumulative effect was evaluated. 1426 respondents followed in four waves were included in FDL model. CES-D score was used to measure depressive symptoms, intergenerational financial support was defined as financial support received from older adults' children or grandchildren. Sociodemographic characteristics, health behaviors, social insurance, and social contact factors were controlled in the model. RESULTS: More than a third older adults in China had a CES-D score of 10 or higher. Intergenerational financial support has a significant long-run cumulative negative effect on older adults' depressive symptoms (CES-D scores: coef. = -0.674, P < 0.001; % with CES-D scores ≥10: Coef. = -0.154, P = 0.018). While, the intergenerational financial support in previous period exhibited a significant negative association with depressive symptoms, the 2, 3, and 4 periods did not reach statistical significance. CONCLUSIONS: Intergenerational financial support has a significant negative effect on older adults' depressive symptoms over time, while the effect may diminish. Programs need to be explored to support home-based eldercare to mitigate this diminished effect.

Smartphone-based versus traditional face-to-face collaborative care for community-dwelling older adults living with dementia in China: protocol for an implementation science-based sequential multiple assignment randomised trial.

INTRODUCTION: The high costs of institutional care and the burdensome demands of home care are challenging for families of adults with dementia. The collaborative care model (CCM) provides a potential solution to these challenges. Leveraging advancements in mobile technologies, smartphone-based management could offer a feasible means of providing collaborative care in a community setting. Therefore, this study aims to establish a CCM for home-cared older adults with dementia to determine the best strategy to deliver collaborative care, including both the channel and frequency of delivery. METHODS AND ANALYSIS: This study will be conducted in the communities of Chengdu city, Sichuan province, China. It is designed under the framework of implementation science. In the first stage, intervention strategies for community-dwelling older adults with dementia and their caregivers will be developed using Delphi methods and focus group interviews. The second stage will involve designing a sequential multiple assignment randomised trial to compare the effectiveness of face-to-face intervention versus a WeChat mini program-based intervention. This comparison will involve 358 pairs of older adults with dementia and their caregivers, with the frequency of intervention also assessed. Follow-up evaluations will be implemented at the 6th, 12th and 18th months post-intervention initiation. Primary outcomes encompass the proportion of patients demonstrating an improvement in quality of life and the proportion of caregivers exhibiting a reduction in caregiver burden. Analysis will be based on the intention-to-treat principle, and the generalised estimating equation approach will be used. Incremental cost-effectiveness ratios will be used to evaluate the cost-effectiveness of different delivery methods and frequencies. ETHICS AND DISSEMINATION: This study has received approval from the Ethics Committee of West China Fourth Hospital/School of Public Health, Sichuan University (Gwll2022004). Informed consent will be obtained for all participants. The findings of the study will be disseminated through peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: ChiCTR2200057945.

Wireless, noninvasive therapeutic drug monitoring system for saliva measurement toward medication management of schizophrenia.

As an efficient patient management tool of precision medicine, decentralized therapeutic drug monitoring (TDM) provides new vision for therapy adherence and health management of schizophrenia in a convenient manner. To dispense with psychologically burdensome blood sampling and to achieve real-time, noninvasive, and continual circulating tracking of drugs with narrow therapeutic window, we study the temporal metabolism of clozapine, an antipsychotic with severe side effect, in rat saliva by a wireless, integrated and patient-friendly smart lollipop sensing system. Highly sensitive and efficient sensing performance with acceptable anti-biofouling property was realized based on the synergistic effect of electrodeposited reduced graphene oxide and ionic liquids in pretreatment-free saliva with low detection limit and good accuracy cross-validated with conventional method. On this basis, continual salivary drug levels with distinctive pharmacokinetics were found in different routes of drug administration. Pilot experiment reveals a strong correlation between blood and saliva clozapine and a positive relationship between drug dosage and salivary drug level, indicating potential applications presented by noninvasive saliva analysis towards patient-centered and personalized pharmacotherapy and adherence management via proposed smart lollipop system.

Investigating Accessibility of Hospitals in Cold Regions: A Case Study of Harbin in China.

OBJECTIVE: The objective of this study is to explore healthcare resource accessibility in Harbin, a typical city in a cold region in China. BACKGROUND: Recently, investments in the construction of medical resources have been increasing annually in China, and consequently, the allocation of these resources has improved. Snow and ice on surfaces in China's cold regions have certain effects on the traffic capacity of urban roads, leading to a great difference in the accessibility of medical resources in winter and summer. METHODS: The basic spatial data, including spatial road data, medical facility data, and population distribution data, are analyzed using geographic information system. Then, a spatial barrier model is used to measure healthcare accessibility based on geographic and population weighting; we explore the accessibility of hospitals under the influence of weather by defining a novel distance attenuation function. Finally, the accessibility of medical institutions in the study area is explored by analyzing data about the related separation factors. RESULTS: It was found that the spatial distribution of medical resources was not equal, and the dominant resources were concentrated in the city center. Some regions are always in an advantageous position regardless of traffic conditions. In contrast, in areas far from the city center, the accessibility of medical resources significantly decreases in winter. CONCLUSIONS: These results will help optimize the layout of medical institutions and improve medical equality and propose strategies for the optimization of the accessibility of urban medical institutions in cold regions of China.

A biosensing system employing nonlinear dynamic analysis-assisted neural network for drug-induced cardiotoxicity assessment.

Preclinical investigation of drug-induced cardiotoxicity is of importance for drug development. To evaluate such cardiotoxicity, in vitro high-throughput interdigitated electrode-based recording of cardiomyocytes mechanical beating is widely used. To automatically analyze the features from the beating signals for drug-induced cardiotoxicity assessment, artificial neural network analysis is conventionally employed and signals are segmented into cycles and feature points are located in the cycles. However, signal segmentation and location of feature points for different signal shapes require design of specific algorithms. Consequently, this may lower the efficiency of research and the applications of such algorithms in signals with different morphologies are limited. Here, we present a biosensing system that employs nonlinear dynamic analysis-assisted neural network (NDANN) to avoid the signal segmentation process and directly extract features from beating signal time series. By processing beating time series with fixed time duration to avoid the signal segmentation process, this NDANN-based biosensing system can identify drug-induced cardiotoxicity with accuracy over 0.99. The individual drugs were classified with high accuracies over 0.94 and drug-induced cardiotoxicity levels were accurately predicted. We also evaluated the generalization performance of the NDANN-based biosensing system in assessing drug-induced cardiotoxicity through an independent dataset. This system achieved accuracy of 0.85-0.95 for different drug concentrations in identification of drug-induced cardiotoxicity. This result demonstrates that our NDANN-based biosensing system has the capacity of screening newly developed drugs, which is crucial in practical applications. This NDANN-based biosensing system can work as a new screening platform for drug-induced cardiotoxicity and improve the efficiency of bio-signal processing.

Analyzing the Spatio-Temporal Dynamics of Urban Land Use Expansion and Its Influencing Factors in Zhejiang Province, China.

The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study's results revealed substantial urban growth of about 6899.59 km2, i.e., 6.6%, whereas agricultural land decreased by 4320.68 km2, i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua-Yiwu, and Wenzhou-Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study's findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area's urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.

Examining the Planning Policies of Urban Villages Guided by China's New-Type Urbanization: A Case Study of Hangzhou City.

Planning policies have greatly influenced the development of urban villages, an informal phenomenon in which rural settlements are encircled by urban environments during China's rapid urbanization process. "The National New-type Urbanization Plan (2014-2020)" of China initiated in 2014 provides a new perspective on planning policy research on China' urban villages. Hangzhou, a pioneer city that adopts new-type urbanization in China and combines the characteristics of rapid urban growth, mountainous urban terrains, and a long cultural history, serves as a typical case study to compare the planning policies responding to the informality of urban villages guided by traditional and new-type urbanization. This study employed the content analysis method to analyze the evolution of Hangzhou's planning policies of urban villages since the reform and opening up and used one-way ANOVA to analyze the differences in rental levels among the urban villages developed under the planning policies of different urbanization stages, aiming to compare the influences of planning policies guided by traditional and new-type urbanization on urban village development. The results indicate that the policies allowing some degree of informality in the new-type urbanization stage achieve a higher rental level for urban villages than the policies of the traditional urbanization stages that restrict and prevent informality. The findings of this research suggest that informality may provide advantages that formality cannot replace and provides important policy implications for rapidly urbanizing countries.

Neratinib plus capecitabine versus lapatinib plus capecitabine as the third-line therapy for HER2-positive metastatic breast cancer in China: a cost-effectiveness analysis.

OBJECTIVE: Neratinib plus capecitabine (Ner+Cap) were proved to be clinically beneficial as a third-line treatment for women with human epidermal growth factor receptor-2 (HER2) positive metastatic breast cancer (MBC). The objective of this study was to evaluate the cost-effectiveness of Ner+Cap from the Chinese healthcare perspective. DESIGN: A three-state Markov simulation model was performed based on the results of NALA trial. The utilities of health state and disutilities of adverse events were derived from the published literature. Direct costs of anticancer agents, drug administration, routine follow-up and serious adverse events management were calculated in the model. Uncertainty was evaluated through univariate and probability sensitivity analysis. PARTICIPANTS: Patients with confirmed HER2-positive MBC who previously received at least two HER2-targeted treatments and were aged ≥18 years with an Eastern Cooperative Oncology Group performance status 0 or 1. A total of 621 patients were enrolled in the NALA trial. INTERVENTIONS: Third-line treatment with Ner+Cap or lapatinib plus capecitabine (Lap+Cap). MAIN OUTCOME MEASURES: The primary health outcomes of the model were costs, expected life-years (LYs), quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs). RESULTS: When compared with Lap+Cap, Ner+Cap provided an additional 0.431 LYs and 0.339 QALYs, and increased the cost by $4299.2. The corresponding ICERs were 9970.1/LY and $12 670.2/QALY. Univariate sensitivity analyses suggested that the results were generally robust. Besides, Ner+Cap had a 100% probability of being cost-effective according to probabilistic sensitivity analysis. CONCLUSIONS: Ner+Cap was likely to be a cost-effective regimen as the third-line therapy for women with HER2-positive MBC at the willingness-to-pay threshold of $37 653.0/QALY in China.

Ecological Smart City Construction Based on Ecological Economy and Network Governance.

China has paid huge resources and environmental costs in the rapid economic development, which severely restricts China's sustainable development. As a mesolevel between macro(state) and micro(enterprise), urban management has many features that are different from traditional management. This research mainly discusses the construction of ecological smart city based on ecological economy and network governance. This research analyzes the current situation and problems of urban construction from three aspects: urban ecological economy, urban ecological environment, and urban ecological society. The ecological indicators of smart cities are used to reflect the true situation of the target. In order to facilitate quantitative analysis with the greatest possibility and accuracy, a batch of representative, comprehensive, and quantifiable indicator data is the key. By drawing on the existing literature and implementing it under the circumstances, the selected methods are frequency analysis and theoretical analysis, which are divided into three parts: economy, environment, and society to construct an urban ecological evaluation index system. Under the new network governance environment, the openness of government affairs has become more transparent and time-effective. Moving the government's work to the network not only enhances the construction of a clean government, but also increases the level of public participation. The main ways of citizen participation created by network governance are carrying out online voting, making anonymous online speeches, and online inquiries about politics, etc. These ways have expanded the traditional government governance methods. Not only that, network governance can make the government's management process more transparent. In this situation, government activities are carried out under the supervision of citizens, which can alleviate the phenomenon of corruption. After the implementation of the ecological smart city plan, the green coverage rate of the built-up area of City A will increase by 1.8% compared with 2017, showing an upward trend. This research will provide effective guidance for the development of ecological smart cities.

Population pharmacokinetics and dosing optimization of mezlocillin in neonates and young infants.

OBJECTIVES: Mezlocillin is used in the treatment of neonatal infectious diseases. However, due to the absence of population pharmacokinetic studies in neonates and young infants, dosing regimens differ considerably in clinical practice. Hence, this study aimed to describe the pharmacokinetic characteristics of mezlocillin in neonates and young infants, and propose the optimal dosing regimen based on the population pharmacokinetic model of mezlocillin. METHODS: A prospective, open-label pharmacokinetic study of mezlocillin was carried out in newborns. Blood samples were collected using an opportunistic sampling method. HPLC was used to measure the plasma drug concentrations. A population pharmacokinetic model was developed using NONMEM software. RESULTS: Ninety-five blood samples from 48 neonates and young infants were included. The ranges of postmenstrual age and birth weight were 29-40 weeks and 1200-4000 g, respectively, including term and preterm infants. A two-compartment model with first-order elimination was developed to describe the population pharmacokinetics of mezlocillin. Postmenstrual age, current weight and serum creatinine concentration were the most important covariates. Monte Carlo simulation results indicated that the current dose of 50 mg/kg q12h resulted in 89.2% of patients achieving the therapeutic target, when the MIC of 4 mg/L was used as the breakpoint. When increasing the dosing frequency to q8h, a dose of 20 mg/kg resulted in 74.3% of patients achieving the therapeutic target. CONCLUSIONS: A population pharmacokinetic model of mezlocillin in neonates and young infants was established. Optimal dosing regimens based on this model were provided for use in neonatal infections.

Predictive Performance of Pharmacokinetic Model-Based Virtual Trials of Vancomycin in Neonates: Mathematics Matches Clinical Observation.

BACKGROUND AND OBJECTIVE: Vancomycin is frequently used to treat Gram-positive bacterial infections in neonates. However, there is still no consensus on the optimal initial dosing regimen. This study aimed to assess the performance of pharmacokinetic model-based virtual trials to predict the dose-exposure relationship of vancomycin in neonates. METHODS: The PubMed database was searched for clinical trials of vancomycin in neonates that reported the percentage of target attainment. Monte Carlo simulations were performed using nonlinear mixed-effect modeling to predict the dose-exposure relationship, and the differences in outcomes between virtual trials and real-world data in clinical studies were calculated. RESULTS: A total of 11 studies with 14 dosing groups were identified from the literature to evaluate dose-exposure relationships. For the ten dosing groups where the surrogate marker for exposure was the trough concentration, the mean ± standard deviation (SD) for the target attainment between original studies and virtual trials was 3.0 ± 7.3%. Deviations between - 10 and 10% accounted for 80% of the included dosing groups. For the other four dosing groups where the surrogate marker for exposure was concentration during continuous infusion, all deviations were between - 10 and 10%, and the mean ± SD value was 2.9 ± 4.5%. CONCLUSION: The pharmacokinetic model-based virtual trials of vancomycin exhibited good predictive performance for dose-exposure relationships in neonates. These results might be used to assist the optimization of dosing regimens in neonatal practice, avoiding the need for trial and error.

Multi-labeled neural network model for automatically processing cardiomyocyte mechanical beating signals in drug assessment.

High-throughput cardiotoxicity assessment is important for large-scale preclinical screening in novel drug development. To improve the efficiency of drug development and avoid drug-induced cardiotoxicity, there is a huge demand to explore the automatic and intelligent drug assessment platforms for preclinical cardiotoxicity investigations. In this work, we proposed an automatic and intelligent strategy that combined automatic feature extraction and multi-labeled neural network (MLNN) to process cardiomyocytes mechanical beating signals detected by an interdigital electrode biosensor for the assessment of drug-induced cardiotoxicity. Taking advantages of artificial neural network, our work not only classified different drugs inducing different cardiotoxicities but also predicted drug concentrations representing severity of cardiotoxicity. This has not been achieved by conventional strategies like principal component analysis and visualized heatmap. MLNN analysis showed high accuracy (up to 96%) and large AUC (more than 98%) for classification of different drug-induced cardiotoxicities. There was a high correlation (over 0.90) between concentrations reported by MLNN and experimentally treated concentrations of various drugs, demonstrating great capacity of our intelligent strategy to predict the severity of drug-induced cardiotoxicity. This new intelligent bio-signal processing algorithm is a promising method for identification and classification of drug-induced cardiotoxicity in cardiological and pharmaceutical applications.

Engineered assembly of water-dispersible nanocatalysts enables low-cost and green CO2 capture.

Catalytic solvent regeneration has attracted broad interest owing to its potential to reduce energy consumption in CO2 separation, enabling industry to achieve emission reduction targets of the Paris Climate Accord. Despite recent advances, the development of engineered acidic nanocatalysts with unique characteristics remains a challenge. Herein, we establish a strategy to tailor the physicochemical properties of metal-organic frameworks (MOFs) for the synthesis of water-dispersible core-shell nanocatalysts with ease of use. We demonstrate that functionalized nanoclusters (Fe3O4-COOH) effectively induce missing-linker deficiencies and fabricate mesoporosity during the self-assembly of MOFs. Superacid sites are created by introducing chelating sulfates on the uncoordinated metal clusters, providing high proton donation capability. The obtained nanomaterials drastically reduce the energy consumption of CO2 capture by 44.7% using only 0.1 wt.% nanocatalyst, which is a ∽10-fold improvement in efficiency compared to heterogeneous catalysts. This research represents a new avenue for the next generation of advanced nanomaterials in catalytic solvent regeneration.

Towards a generic prototyping approach for therapeutically-relevant peptides and proteins in a cell-free translation system.

Advances in peptide and protein therapeutics increased the need for rapid and cost-effective polypeptide prototyping. While in vitro translation systems are well suited for fast and multiplexed polypeptide prototyping, they suffer from misfolding, aggregation and disulfide-bond scrambling of the translated products. Here we propose that efficient folding of in vitro produced disulfide-rich peptides and proteins can be achieved if performed in an aggregation-free and thermodynamically controlled folding environment. To this end, we modify an E. coli-based in vitro translation system to allow co-translational capture of translated products by affinity matrix. This process reduces protein aggregation and enables productive oxidative folding and recycling of misfolded states under thermodynamic control. In this study we show that the developed approach is likely to be generally applicable for prototyping of a wide variety of disulfide-constrained peptides, macrocyclic peptides with non-native bonds and antibody fragments in amounts sufficient for interaction analysis and biological activity assessment.